From 63f1eccbfdefdc8d451d867a9b15774b933af79d Mon Sep 17 00:00:00 2001 From: Ryan Mullins Date: Tue, 22 Oct 2024 19:22:46 +0000 Subject: [PATCH 1/2] Updating website for v1.3 launch --- docs/demos/coref.html | 5 - docs/demos/images.html | 5 - docs/demos/index.html | 55 - docs/demos/is_eval.html | 5 - docs/demos/lm.html | 5 - docs/demos/t5.html | 5 - docs/documentation/_images/attention.png | Bin 188417 -> 0 bytes docs/documentation/_images/lit-attention.png | Bin 91620 -> 0 bytes .../_images/lit-colab-server-address.png | Bin 0 -> 28883 bytes .../_images/lit-datapoint-compare.png | Bin 459728 -> 368329 bytes .../documentation/_images/lit-s2s-journey.png | Bin 147695 -> 0 bytes .../_images/lit-ui-error-in-colab.png | Bin 0 -> 124899 bytes .../_images/lit-winogender-metrics.png | Bin 160995 -> 0 bytes docs/documentation/_images/lit-winogender.png | Bin 254066 -> 0 bytes docs/documentation/_images/pair-selection.png | Bin 34353 -> 159071 bytes docs/documentation/_sources/api.md.txt | 60 +- docs/documentation/_sources/components.md.txt | 89 +- docs/documentation/_sources/demos.md.txt | 99 +- docs/documentation/_sources/docker.md.txt | 34 +- docs/documentation/_sources/faq.md.txt | 75 +- .../_sources/frontend_development.md.txt | 16 +- .../_sources/getting_started.md.txt | 5 +- docs/documentation/_sources/index.md.txt | 2 +- docs/documentation/_sources/ui_guide.md.txt | 114 +- docs/documentation/_static/basic.css | 15 +- docs/documentation/_static/doctools.js | 7 - docs/documentation/_static/language_data.js | 9 +- docs/documentation/_static/pygments.css | 12 +- docs/documentation/_static/scripts/furo.js | 2 +- .../documentation/_static/scripts/furo.js.map | 2 +- docs/documentation/_static/searchtools.js | 200 +- .../_static/styles/furo-extensions.css | 2 +- .../_static/styles/furo-extensions.css.map | 2 +- docs/documentation/_static/styles/furo.css | 2 +- .../documentation/_static/styles/furo.css.map | 2 +- docs/documentation/api.html | 216 +- docs/documentation/components.html | 257 +- docs/documentation/demos.html | 226 +- docs/documentation/docker.html | 134 +- docs/documentation/faq.html | 193 +- docs/documentation/frontend_development.html | 136 +- docs/documentation/genindex.html | 85 +- docs/documentation/getting_started.html | 113 +- docs/documentation/glossary.html | 94 +- .../includes/highlight_demos.html | 92 +- docs/documentation/index.html | 103 +- docs/documentation/search.html | 90 +- docs/documentation/searchindex.js | 2 +- docs/documentation/ui_guide.html | 255 +- docs/index.html | 3 +- docs/tutorials/coref/index.html | 187 - docs/tutorials/generation/index.html | 149 - docs/tutorials/index.html | 8 - docs/tutorials/sentiment/index.html | 2 +- docs/tutorials/sequence-salience/index.html | 17 +- docs/tutorials/tab-feat-attr/index.html | 2 +- docs/tutorials/text-salience/index.html | 2 +- package-lock.json | 6 + website/package-lock.json | 8662 ----------------- 59 files changed, 1588 insertions(+), 10275 deletions(-) delete mode 100644 docs/demos/coref.html delete mode 100644 docs/demos/images.html delete mode 100644 docs/demos/is_eval.html delete mode 100644 docs/demos/lm.html delete mode 100644 docs/demos/t5.html delete mode 100644 docs/documentation/_images/attention.png delete mode 100644 docs/documentation/_images/lit-attention.png create mode 100644 docs/documentation/_images/lit-colab-server-address.png delete mode 100644 docs/documentation/_images/lit-s2s-journey.png create mode 100644 docs/documentation/_images/lit-ui-error-in-colab.png delete mode 100644 docs/documentation/_images/lit-winogender-metrics.png delete mode 100644 docs/documentation/_images/lit-winogender.png delete mode 100644 docs/tutorials/coref/index.html delete mode 100644 docs/tutorials/generation/index.html create mode 100644 package-lock.json delete mode 100644 website/package-lock.json diff --git a/docs/demos/coref.html b/docs/demos/coref.html deleted file mode 100644 index 1de185ef..00000000 --- a/docs/demos/coref.html +++ /dev/null @@ -1,5 +0,0 @@ - - - \ No newline at end of file diff --git a/docs/demos/images.html b/docs/demos/images.html deleted file mode 100644 index ce72cef5..00000000 --- a/docs/demos/images.html +++ /dev/null @@ -1,5 +0,0 @@ - - - \ No newline at end of file diff --git a/docs/demos/index.html b/docs/demos/index.html index 546221f4..3a33dc86 100644 --- a/docs/demos/index.html +++ b/docs/demos/index.html @@ -98,17 +98,6 @@
Analyze a tabular data model with LIT, including exploring partial dependence plots and automatically finding counterfactuals.
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images multiclass classification -
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DATA SOURCES
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- Imagenette -
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Analyze an image classification model with LIT, including multiple image salience techniques.
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BERT binary classification multi-class classification regression @@ -130,50 +119,6 @@
Use LIT directly inside a Colab notebook. Explore binary classification for sentiment analysis using SST2 from the General Language Understanding Evaluation (GLUE) benchmark suite.
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BERT coreference fairness Winogender -
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DATA SOURCES
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- Winogender schemas -
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Use LIT to explore gendered associations in a coreference system, which matches pronouns to their antecedents. This demo highlights how LIT can work with structured prediction models (edge classification), and its capability for disaggregated analysis.
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BERT masked language model -
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DATA SOURCES
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- Stanford Sentiment Treebank, Movie Reviews -
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Explore a BERT-based masked-language model. See what tokens the model predicts should fill in the blank when any token from an example sentence is masked out.
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T5 generation -
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DATA SOURCES
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- CNN / Daily Mail -
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Use a T5 model to summarize text. For any example of interest, quickly find similar examples from the training set, using an approximate nearest-neighbors index.
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BERT salience evaluation -
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DATA SOURCES
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- Stanford Sentiment Treebank, Toxicity -
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Explore the faithfulness of input salience methods on a BERT-base model across different datasets and artificial shortcuts.
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z`pM~m^**r8p@fBcCc2{XHta^B$`1M~LP zEP800G#FMutI6OoE{!gjh4vo*<=dQ|vD)#?GmfP9sXW>g~x3abm%3Y3#i zjWn*hsuee=;0hREW!!scUwZyGz*eajU zc0Lr&bu-X{VX`BkTM$PJpi3aQ+Gy8wF%{H%acg=BD#{!=RZtcVUuU&@cOEn6Y1jNmSztiODx z_-AxsvMoP5j>O-VtY>NHps~6~Kfz@iG2fCT9z_b8hxdaj{hUE2D5x%F7;NdK2nZ+# zJdT#q5TP{@e-oZPQq*Qc*iTTxvS>#hpjcJNG5c`}0M%tLjZSH*t||igFUIm+-o zg?j#7_EyYh z8zc_#%3L_h-N^d+h;j-uzBQoHSchQS5vmV^Wmi|Mh9E*O_`Vp zc&5?}{P!<$U}81F;t9V|)&gHT_{VD@(99*mOGs0E``3=NF|($BxUEGSSZN~HBjoP* z_E@&$;Y@S>twNnwSM|d~JCYDwWEnlp%9**w?u%#P`DeC;5W0icUE40ekcPAr(w|+{I7gK$oID33y sCt(od?8@jFI5zkH7K#XELwxlCnYK1;<-DE + @@ -349,7 +349,7 @@ list of scores for each token. The Integrated Gradients saliency method additionally requires a `TokenEmbeddings` input and corresponding output, as well as a label field `Target` to pin the gradient target to the same class as an input and corresponding output. See the -[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py) +[GLUE models class](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py) for an example of these spec requirements. The core API involves implementing the `run()` method: @@ -675,7 +675,7 @@ Each `LitType` subclass encapsulates its own semantics (see * A field that appears in _both_ the model's input and output specs is assumed to represent the same value. This pattern is used for model-based input manipulation. For example, a - [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py) + [language model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py) might output `'tokens': lit_types.Tokens(...)`, and accept as (optional) input `'tokens': lit_types.Tokens(required=False, ...)`. An interpretability component could take output from the former, swap one or more tokens (e.g. @@ -712,11 +712,9 @@ this can cause jitter (UI modules appearing, disappearing, reordering, resizing, etc.) when switching between models or datasets with heterogeneous `Spec`s. When implementing your own LIT components and modules, you can use -[`utils.find_spec_keys()`][utils-lib] -(Python) and -[`findSpecKeys()`][utils-lib] -(TypeScript) to identify fields of interest in a `Spec`. These methods recognize -and respect subclasses. For example, +[`utils.find_spec_keys()`][utils-lib-py] (Python) and +[`findSpecKeys()`][utils-lib] (TypeScript) to identify fields of interest in a +`Spec`. These methods recognize and respect subclasses. For example, `utils.find_spec_keys(spec, Scalar)` will also match any `RegressionScore` fields, but `utils.find_spec_keys(spec, RegressionScore)` will not return all `Scalar` fields in the `Spec`. @@ -807,8 +805,13 @@ _See the [examples](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples) ### Available types -The full set of `LitType`s is defined in [types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types such as `Integer` and `Scalar` have predefined ranges that can be overridden using corresponding `min_val` and `max_val` attributes as seen [here](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/penguin_data.py;l=19-22;rcl=574999438). The different types available in LIT are summarized -in the table below. +The full set of `LitType`s is defined in +[types.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py). Numeric types +such as `Integer` and `Scalar` have predefined ranges that can be overridden +using corresponding `min_val` and `max_val` attributes as seen in +[penguin data](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/data.py) +`INPUT_SPEC`. The different types available in LIT are summarized in the table +below. Note: Bracket syntax, such as `[num_tokens]`, refers to the shapes of NumPy arrays where each element inside the brackets is an integer. @@ -859,7 +862,7 @@ naming collisions with protected TypeScript keywords.* Some properties of the LIT frontend can be configured from Python as **arguments to `dev_server.Server()`**. These include: -* `page_title`: set a custom page title, such as "Coreference Demo". +* `page_title`: set a custom page title. * `canonical_url`: set a "canonical" URL (such as a shortlink) that will be used as the base when copying links from the LIT UI. * `default_layout`: set the default UI layout, by name. See `layout.ts` and @@ -886,22 +889,16 @@ You can specify custom web app layouts from Python via the `layouts=` attribute. The value should be a `Mapping[str, LitCanonicalLayout]`, such as: ```python -LM_LAYOUT = layout.LitCanonicalLayout( +PENGUIN_LAYOUT = layout.LitCanonicalLayout( upper={ - "Main": [ - modules.EmbeddingsModule, + 'Main': [ + modules.DiveModule, modules.DataTableModule, modules.DatapointEditorModule, ] }, - lower={ - "Predictions": [ - modules.LanguageModelPredictionModule, - modules.ConfusionMatrixModule, - ], - "Counterfactuals": [modules.GeneratorModule], - }, - description="Custom layout for language models.", + lower=layout.STANDARD_LAYOUT.lower, + description='Custom layout for the Palmer Penguins demo.', ) ``` @@ -912,14 +909,12 @@ lit_demo = dev_server.Server( models, datasets, # other args... - layouts={"lm": LM_LAYOUT}, + layouts=layout.DEFAULT_LAYOUTS | {'penguins': PENGUIN_LAYOUT}, + default_layout='penguins', **server_flags.get_flags()) return lit_demo.serve() ``` -For a full example, see -[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py). - You can see the pre-configured layouts provided by LIT, as well as the list of modules that can be included in your custom layout in [`layout.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/layout.py). A @@ -989,15 +984,15 @@ needing to reload the server or click the UI. For example, to view examples in a dataset: ```python -from lit_nlp.examples.datasets import glue -dataset = glue.SST2Data('validation') +from lit_nlp.examples.glue import data as glue_data +dataset = glue_data.SST2Data('validation') print(dataset.examples) # list of records {"sentence": ..., "label": ...} ``` And to run inference on a few of them: ```python -from lit_nlp.examples.models import glue_models +from lit_nlp.examples.glue import models as glue_models model = glue_models.SST2Model("/path/to/model/files") preds = list(model.predict(dataset.examples[:5])) @@ -1021,16 +1016,19 @@ For a full working example in Colab, see [LIT_components_example.ipynb](https:// [build-metadata]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/app.py -[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py +[components-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/components.py [curves-interp]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/curves.py [dataset-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py [grad-maps]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/gradient_maps.py [json]: https://www.json.org [mnli-dataset]: https://cims.nyu.edu/~sbowman/multinli/ + [mnli-demo]: https://pair-code.github.io/lit/demos/glue.html -[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/dataset.py + +[model-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/model.py [should_display_module]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/lit_module.ts [types_py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/api/types.py [types_ts]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/lit_types.ts [utils-lib]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/lib/utils.ts +[utils-lib-py]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/lib/utils.py [word-replacer]: https://github.com/PAIR-code/lit/blob/main/lit_nlp/components/word_replacer.py diff --git a/docs/documentation/_sources/components.md.txt b/docs/documentation/_sources/components.md.txt index f2366d7c..90b99dbd 100644 --- a/docs/documentation/_sources/components.md.txt +++ b/docs/documentation/_sources/components.md.txt @@ -1,6 +1,6 @@ # Components and Features - + @@ -44,10 +44,9 @@ every invocation.) Generally, you'll need to: directly. Alternatively, you can export to a `SavedModel` and load this in an eager mode -runtime. This leads to much simpler code (see -[this example](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py?l=72&rcl=378713625)), -but may require changes to your `SavedModel` exporter in order to access model -internals like embeddings, gradients, or attention. +runtime. This leads to much simpler code, but may require changes to your +`SavedModel` exporter in order to access model internals like embeddings, +gradients, or attention. ### Remote or hosted models @@ -105,8 +104,6 @@ LIT supports many features for classification tasks, including common metrics, confusion matrices, and custom thresholding via the UI. Classification is implemented with the `MulticlassPreds` and `CategoryLabel` types. -* For a basic example on a binary sentiment task, see - [examples/simple_tf2_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/simple_tf2_demo.py). * Models should define a `MulticlassPreds` field in their output spec with the `vocab=` attribute as the set of class labels, and for each example should return a vector of probabilities for each class. @@ -116,9 +113,7 @@ implemented with the `MulticlassPreds` and `CategoryLabel` types. * A negative class can be designated using the `null_idx` attribute of `MulticlassPreds` (most commonly, `null_idx=0`), and metrics such as precision, recall, F1 will be computed for the remaining classes. AUC and - AUCPR will be computed for binary classification tasks. For an example, see - the - [comment toxicity model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py?l=518&rcl=386779180). + AUCPR will be computed for binary classification tasks. * If `null_idx` is set and there is only one other class, the other class (often, class `1`) is treated as a positive class, and the LIT UI can be used to change the classification threshold. If `null_idx` is set and there @@ -139,7 +134,7 @@ faceting, and scatterplots of scalar output. Regression is implemented with the which contains numerical targets, and the model's `RegressionScore` field should set `parent=` to the name of this field. * For an example, see the STS-B textual similarity task in - [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py). + [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py). ### Multi-label classification @@ -147,8 +142,6 @@ LIT supports multi-label tasks, when a model can label a single example with more than one label. Multi-label classification is implemented with the `SparseMultilabelPreds` and `SparseMultilabel` types. -* For a basic example on an image labeling task, see - [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py). * Models should define a `SparseMultilabelPreds` field in their output spec with the`vocab=` attribute as the set of class labels, and for each example should return a list of class score tuples. Each tuple contains two @@ -179,8 +172,6 @@ or decoder. the model can also output a `ReferenceScores` field (with values as `list[float]`) with `parent=` set to reference a `TextSegment` or `ReferenceTexts` field from the input. -* For modeling examples, see - [examples/models/t5.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/t5.py) ![Generated Text Module](images/components/generation-results.png){w=600px align=center} @@ -215,7 +206,7 @@ and otherwise to different parts of the input. [Dataset class documentation](./api.md#datasets) and the corresponding [Model](./api.md#models). * For a more involved code example including per-token gradients, see - [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py). + [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py). ### Tabular data @@ -233,7 +224,7 @@ these cases, LIT validates model compatibility by checking that: (`SparseMultilabel`) field. For a demo using a penguin stats dataset/binary classification task, see -[examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py). +[examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py). ### Images @@ -246,12 +237,6 @@ NOTE: We may transition images away from encoded strings, moving to individual pixel color values. We will ensure we don't break existing checked-in code with such a change. -* See [examples/datasets/open_images.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/datasets/open_images.py) for a - dataset containing images, including converting images to base64 encoded - strings. -* For a demo of an image classifier, see - [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py). - ## Token-based Salience LIT supports several methods for token-based input salience, including @@ -301,8 +286,8 @@ $$S(i) \propto x_i \cdot \nabla_{x_i} \hat{y}$$ Compared to grad-norm, this gives directional scores: a positive score is can be interpreted as that token having a positive influence on the prediction -$\hat{y}$, while a negative score suggests that the prediction would be stronger -if that token was removed. +$\hat{y}$, while a negative score suggests that the prediction would be +stronger if that token was removed. To enable this method, your model should, as part of the [output spec and `predict()` implementation](./api.md#models): @@ -366,7 +351,7 @@ An example spec would look like: For a more concrete example that also supports multiple segments with separate gradients, see our -[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py), +[BERT classifier demo model](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py), or contact the LIT team for assistance. ### LIME @@ -448,7 +433,7 @@ left-to-right ("causal") language models: The UI supports multiple options for analysis, including: -* Select from pre-defined target sequences, or explain generations from the +* Select from predefined target sequences, or explain generations from the model. * Different salience methods, including [Gradient Norm](#gradient-norm) and [Gradient-dot-Input](#gradient-dot-input). @@ -456,7 +441,7 @@ The UI supports multiple options for analysis, including: to words, sentences, lines, or paragraphs. Quickly switch between different views to refine your analysis to different parts of a prompt. * Display density options to enable working with longer sequences, such as - document text, few-shot eaxmples, or chain-of-thought prompts. + document text, few-shot examples, or chain-of-thought prompts. For a walkthrough of how to use sequence salience to debug LLMs, check out the Responsible Generative AI Toolkit at @@ -482,13 +467,11 @@ using either KerasNLP or Transformers. * LIT-for-Gemma Colab: [`lit_gemma.ipynb`](https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemma/docs/lit_gemma.ipynb) * Demo binary: - [`lm_salience_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py) + https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py * KerasNLP model wrappers: - [`instrumented_keras_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/instrumented_keras_lms.py) + https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/keras_lms.py * Transformers model wrappers: - [`pretrained_lms.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/pretrained_lms.py) - - + https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/transformers_lms.py ## Salience Clustering @@ -540,9 +523,7 @@ your model should, as part of the input image. The model should be able to accept input images as numpy arrays in addition - to accepting base64 URL encoded format. See - [mobilenet.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/mobilenet.py) - for an example. + to accepting base64 URL encoded format. A variety of image saliency techniques are implemented for models that return image gradients, through use of the @@ -553,36 +534,6 @@ and XRAI. Each of these techniques returns a saliency map image as a base64-encoded string through the `ImageSalience` type. -## Attention - -LIT can display a visualization of attention heads from transformers and other -models: - -![Attention Visualization](./images/components/attention.png){w=400px align=center} - -To enable this, your model should return one or more fields of the type -`AttentionHeads`, with values as arrays of shape `[num_heads, num_tokens, -num_tokens]`. Each field represents a set of heads at a single layer of the -model, so models will often have more than one: - -```python - def output_spec(self) -> types.Spec: - return { - # ... - "tokens": lit_types.Tokens(parent="input_text"), - "layer_0/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"), - "layer_1/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"), - "layer_2/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"), - # ... - } -``` - -The `align_in` and `align_out` attributes are the names of `Tokens` fields for -the source and target tokens for that layer. For self-attention (such as in -BERT) these would name the same field, but for encoder-decoder attention they -could reference different token sets (such as "input_tokens" and -"target_tokens"). - ## Embedding Projector LIT includes a version of the @@ -614,7 +565,7 @@ example, we could facet by class label: ![Metrics Table](./images/components/metrics-table.png) -To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab. +To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab. To enable metrics, your model should set the `parent` attribute on one or more output fields, pointing to the name of the input field that it should be @@ -653,7 +604,7 @@ analysis. ![Confusion Matrix](./images/components/confusion-matrix.png){w=600px align=center} -To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Performance" tab. +To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the "Metrics" tab. The confusion matrix is supported for classification models, or if the input data includes any categorical features (`CategoryLabel`). @@ -739,6 +690,8 @@ datapoints, giving a global view of feature effects. ![Partial Dependence Plots Module](./images/components/lit-pdps.png){w=400px align=center} +To try this out, see https://pair-code.github.io/lit/demos/penguins.html and navigate to the "Predictions" tab. + ### Dive Dive is a visualization module, inspired by our prior work on diff --git a/docs/documentation/_sources/demos.md.txt b/docs/documentation/_sources/demos.md.txt index 306d3139..448c0d23 100644 --- a/docs/documentation/_sources/demos.md.txt +++ b/docs/documentation/_sources/demos.md.txt @@ -1,6 +1,6 @@ # Demos - + @@ -17,7 +17,7 @@ https://pair-code.github.io/lit/demos/. ### Sentiment and NLI **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html \ -**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) +**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py) * Multi-task demo: * Sentiment analysis as a binary classification task @@ -36,26 +36,12 @@ https://pair-code.github.io/lit/demos/. the margin. * Embedding projector to find clusters in representation space. * Integrated Gradients, LIME, and other salience methods. - * Attention visualization. * Counterfactual generators, including HotFlip for targeted adversarial perturbations. Tip: check out a case study for this demo on the public LIT website: https://pair-code.github.io/lit/tutorials/sentiment -### Multilingual (XNLI) - -**Code:** [examples/xnli_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py) - -* [XNLI](https://cims.nyu.edu/~sbowman/xnli/) dataset translates a subset of - MultiNLI into 14 different languages. -* Specify `--languages=en,jp,hi,...` flag to select which languages to load. -* NLI as a three-way classification task with two-segment input (premise, - hypothesis). -* Fine-tuned multilingual BERT model. -* Salience methods work with non-whitespace-delimited text, by using the - model's wordpiece tokenization. - -------------------------------------------------------------------------------- ## Regression / Scoring @@ -63,7 +49,7 @@ https://pair-code.github.io/lit/tutorials/sentiment ### Textual Similarity (STS-B) **Hosted instance:** https://pair-code.github.io/lit/demos/glue.html?models=stsb&dataset=stsb_dev \ -**Code:** [examples/glue_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) +**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py) * STS-B textual similarity task, predicting scores on a range from 0 (unrelated) to 5 (very similar). @@ -74,7 +60,6 @@ https://pair-code.github.io/lit/tutorials/sentiment quick filtering. * Embedding projector to find clusters in representation space. * Integrated Gradients, LIME, and other salience methods. - * Attention visualization. -------------------------------------------------------------------------------- @@ -82,76 +67,20 @@ https://pair-code.github.io/lit/tutorials/sentiment ### Gemma -**Code:** [examples/lm_salience_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_salience_demo.py) +**Code:** +[examples/prompt_debugging/server.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py) -* Supports Gemma 2B and 7B models using KerasNLP and TensorFlow. +* Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch) + and Transformers (with PyTorch). * Interactively debug LLM prompts using [sequence salience](./components.md#sequence-salience). * Multiple salience methods (grad-l2 and grad-dot-input), at multiple - granularities: token-, word-, sentence-, and paragraph-level. + granularities: token-, word-, line-, sentence-, and paragraph-level. Tip: check out the in-depth walkthrough at https://ai.google.dev/responsible/model_behavior, part of the Responsible Generative AI Toolkit. -### T5 - -**Hosted instance:** https://pair-code.github.io/lit/demos/t5.html \ -**Code:** [examples/t5_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py) - -* Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel - formats. -* Visualize beam candidates and highlight diffs against references. -* Visualize per-token decoder hypotheses to see where the model veers away - from desired output. -* Filter examples by ROUGE score against reference. -* Embeddings from last layer of model, visualized with UMAP or PCA. -* Task wrappers to handle pre- and post-processing for summarization and - machine translation tasks. -* Pre-loaded eval sets for CNNDM and WMT. - -Tip: check out a case study for this demo on the public LIT website: -https://pair-code.github.io/lit/tutorials/generation - --------------------------------------------------------------------------------- - -## Language Modeling - -### BERT and GPT-2 - -**Hosted instance:** https://pair-code.github.io/lit/demos/lm.html \ -**Code:** [examples/lm_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py) - -* Compare multiple BERT and GPT-2 models side-by-side on a variety of - plain-text corpora. -* LM visualization supports different modes: - * BERT masked language model: click-to-mask, and query model at that - position. - * GPT-2 shows left-to-right hypotheses for each target token. -* Embedding projector to show latent space of the model. - --------------------------------------------------------------------------------- - -## Structured Prediction - -### Gender Bias in Coreference - -**Hosted instance:** https://pair-code.github.io/lit/demos/coref.html \ -**Code:** [examples/coref/coref_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/coref/coref_demo.py) - -* Gold-mention coreference model, trained on - [OntoNotes](https://catalog.ldc.upenn.edu/LDC2013T19). -* Evaluate on the Winogender schemas - ([Rudinger et al. 2018](https://arxiv.org/abs/1804.09301)) which test for - gendered associations with profession names. -* Visualizations of coreference edges, as well as binary classification - between two candidate referents. -* Stratified metrics for quantifying model bias as a function of pronoun - gender or Bureau of Labor Statistics profession data. - -Tip: check out a case study for this demo on the public LIT website: -https://pair-code.github.io/lit/tutorials/coref - -------------------------------------------------------------------------------- ## Multimodal @@ -159,7 +88,7 @@ https://pair-code.github.io/lit/tutorials/coref ### Tabular Data: Penguin Classification **Hosted instance:** https://pair-code.github.io/lit/demos/penguins.html \ -**Code:** [examples/penguin_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) +**Code:** [examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py) * Binary classification on [penguin dataset](https://www.tensorflow.org/datasets/catalog/penguins). @@ -169,13 +98,3 @@ https://pair-code.github.io/lit/tutorials/coref * Use binary classifier threshold setters to find best thresholds for slices of examples to achieve specific fairness constraints, such as demographic parity. - -### Image Classification with MobileNet - -**Hosted instance:** https://pair-code.github.io/lit/demos/images.html \ -**Code:** [examples/image_demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py) - -* Classification on ImageNet labels using a MobileNet model. -* Showing using of LIT on image data. -* Explore results of multiple gradient-based image saliency techniques in the - Salience Maps module. diff --git a/docs/documentation/_sources/docker.md.txt b/docs/documentation/_sources/docker.md.txt index c9cad085..ebf7eb7c 100644 --- a/docs/documentation/_sources/docker.md.txt +++ b/docs/documentation/_sources/docker.md.txt @@ -1,6 +1,6 @@ # Running LIT in a Docker container - + Users might want to deploy LIT onto servers for public-facing, long-running instances. This is how we host the LIT demos found on @@ -13,22 +13,21 @@ LIT can be run as a containerized app using [Docker](https://www.docker.com/) or your preferred engine. This is how we run our [hosted demos](https://pair-code.github.io/lit/demos/). -We provide a basic -[`Dockerfile`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/Dockerfile) that you can -use to build and run any of the demos in the `lit_nlp/examples` directory. The -`Dockerfile` installs all necessary dependencies for LIT and builds the +We provide a basic Dockerfile https://github.com/PAIR-code/lit/blob/main/Dockerfile that you can use to build and run any of the demos in the `lit_nlp/examples` directory. +The `Dockerfile` installs all necessary dependencies for LIT and builds the front-end code from source. Then it runs [gunicorn](https://gunicorn.org/) as the HTTP server, invoking the `get_wsgi_app()` method from our demo file to get the WSGI app to serve. The options provided to gunicorn for our use-case can be found in [`gunicorn_config.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/gunicorn_config.py). You can find a reference implementation in -[`glue_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue_demo.py) or -[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py). +[`glue/demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py). -Use the following shell commands to build the default Docker image for LIT from -the provided `Dockerfile`, and then run a container from that image. Comments -are provided in-line to help explain what each step does. +Use the following shell +https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml commands to build the +default Docker image for LIT from the provided `Dockerfile`, and then run a +container from that image. Comments are provided in-line to help explain what +each step does. ```shell # Build the docker image using the -t argument to name the image. Remember to @@ -48,14 +47,11 @@ below. ```shell # DEMO_NAME is used to complete the Python module path # -# "lit_nlp.examples.$DEMO_NAME" +# "lit_nlp.examples.$DEMO_NAME.demo:get_wsgi_app()" # # Therefore, valid values for DEMO_NAME are Python module paths in the -# lit_nlp/examples directory, such as -# -# * direct children -- glue_demo, lm_demo, image_demo, t5_demo, etc. -# * And nested children -- coref.coref_demo, is_eval.is_eval_demo, etc. -docker run --rm -p 5432:5432 -e DEMO_NAME=lm_demo lit-nlp +# lit_nlp/examples directory, such as glue, penguin, tydi, etc. +docker run --rm -p 5432:5432 -e DEMO_NAME=penguin lit-nlp # Use the DEMO_PORT environment variable as to change the port that LIT uses in # the container. Be sure to also change the -p option to map the container's @@ -66,15 +62,15 @@ docker run --rm -p 2345:2345 -e DEMO_PORT=2345 lit-nlp # containers on your machine using the combination of the DEMO_NAME and # DEMO_PORT arguments, and docker run with the -d flag to run the container in # the background. -docker run -d -p 5432:5432 -e DEMO_NAME=t5_demo lit-nlp -docker run -d -p 2345:2345 -e DEMO_NAME=lm_demo -e DEMO_PORT=2345 lit-nlp +docker run -d -p 5432:5432 -e DEMO_NAME=penguin lit-nlp +docker run -d -p 2345:2345 -e DEMO_NAME=tydi -e DEMO_PORT=2345 lit-nlp ``` ## Integrating Custom LIT Instances with the Default Docker Image Many LIT users create their own custom LIT server script to demo or serve, which involves creating an executable Python module with a `main()` method, as -described in the [Python API docs](https://pair-code.github.io/lit/documentation/api.md#adding-models-and-data). +described in the [Python API docs](api.md#adding-models-and-data). These custom server scripts can be easily integrated with LIT's default image as long as your server meets two requirements: diff --git a/docs/documentation/_sources/faq.md.txt b/docs/documentation/_sources/faq.md.txt index de6a5b57..8e334b24 100644 --- a/docs/documentation/_sources/faq.md.txt +++ b/docs/documentation/_sources/faq.md.txt @@ -1,6 +1,6 @@ # Frequently Asked Questions - + @@ -17,9 +17,7 @@ works with any modern ML framework. For more information, see In addition to text, LIT has good support for different modalities, including images and tabular data. For examples, see: -* [Image demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/image_demo.py) - - image classification, using a Mobilenet model. -* [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin_demo.py) - +* [Tabular demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py) - multi-class classification on tabular (numeric and categorical string) data, using the [Palmer Penguins](https://www.tensorflow.org/datasets/catalog/penguins) @@ -32,13 +30,7 @@ For more details, see All strings in LIT are unicode and most components use model-provided tokenization if available, so in most cases non-English languages and non-Latin -scripts should work without any modifications. For examples, see: - -* [XNLI demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/xnli_demo.py) - - cross-lingual NLI, with up to 15 languages supported via a multilingual BERT - model. -* [T5 demo](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/t5_demo.py) - - includes WMT data for machine translation +scripts should work without any modifications. ## Scale @@ -99,8 +91,8 @@ manually entered in the web UI) are stored in server memory, and if `--data_dir` is specified, may be cached to disk. LIT has the ability to create or edit datapoints in the UI and then save them to -disk. If you do not want the tool to be able to write edited datapoints to -disk, then pass the `--demo_mode` runtime flag to the LIT server. +disk. If you do not want the tool to be able to write edited datapoints to disk, +then pass the `--demo_mode` runtime flag to the LIT server. ### I have proprietary data. Is LIT secure for my team to use? @@ -177,7 +169,58 @@ official training APIs. However, to facilitate code reuse you can easily add training methods to your model class. In fact, several of our demos do exactly this, using LIT's `Dataset` objects to manage training data along with standard training APIs (such as Keras' `model.fit()`). See -[`quickstart_sst_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/quickstart_sst_demo.py) -and/or -[`glue_models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/models/glue_models.py) +[`glue/models.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/models.py) for examples. + +### Debug LIT UI in Colab + +The LIT instance launched from CLI typically has helpful error messages in the +UI. However, this is not the case for the LIT UI in Colab and the error message +does not report any stacktrace, which makes debugging very difficult. + +![LIT UI error in colab](./images/lit-ui-error-in-colab.png "LIT UI error in colab") + +While in +[Chrome developer tools](https://support.google.com/campaignmanager/answer/2828688?hl=en), +you will be able to debug issues solely related to the frontend, but not so for +issues related to the backend or on the HTTP request path. + +Thus, to show the full stacktrace, you would need to find the HTTP request sent +from the frontend to the backend, compose the same request in colab and send it +to the server. + +1. When rendering the UI, display it in a separate tab to make things a bit + easier to work with, e.g. `lit_widget.render(open_in_new_tab=True)`. +2. Open + [Chrome developer tools](https://support.google.com/campaignmanager/answer/2828688?hl=en), + go to "Sources" tab and find the file + [client/services/api_service.ts](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/services/api_service.ts) and set a + breakpoint right after where the HTTP request is set up in the `queryServer` + method, e.g. after this line `const res = await fetch(url, {method: 'POST', + body});`. + * Note it is possible that the whole frontend source code is compiled into + a `main.js` file, and the code is not exactly the same as that in LIT + frontend source code. You might have to do a bit digging to find the + right line. +3. Go to the UI and trigger the behavior that causes the error. Now in Chrome + developer tools you will be able to see the variables and their values in + the `queryServer` method. Copy the values of the `url` and `body` variables + in the method. +4. Go back to Colab, compose your HTTP request method. Look for the main server + address printed out from `lit_widget.render(open_in_new_tab=True)`. + +![LIT colab server address](./images/lit-colab-server-address.png "LIT colab server address") + +Let's say the server address is "https://localhost:32943/?" as shown above, the +`body` variable obtained earlier has value "request_body_text" and the `url` +variable has value "./get_preds?param1=value1". Then your HTTP request will be +like this: + +```sh +! curl -H "Content-Type: application/json" \ + -d "request_body_text" \ + -X POST "http://localhost:32943/get_preds?param1=value1" +``` + +Run this in Colab and you should be able to retrieve the full stacktrace of the +error. diff --git a/docs/documentation/_sources/frontend_development.md.txt b/docs/documentation/_sources/frontend_development.md.txt index a51aed81..fe8a82bb 100644 --- a/docs/documentation/_sources/frontend_development.md.txt +++ b/docs/documentation/_sources/frontend_development.md.txt @@ -1,6 +1,6 @@ # Frontend Developer Guide - + @@ -46,9 +46,9 @@ and rendering the various `LitModule` components, a process about which we'll go into greater detail later. The JS bundle entry point is -[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/default/main.ts), which -first imports the loaded, the `` web component is declared, and -attaches itself to the DOM, waiting for the app to be initialized. +[`main.ts`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/main.ts), which first +imports the loaded, the `` web component is declared, and attaches +itself to the DOM, waiting for the app to be initialized. The second step is kicking off app initialization. The [`LitApp`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/client/core/app.ts) singleton @@ -67,7 +67,7 @@ pre-configured layouts in * `default`: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and -* `experimental`: A three-panel layout that puts exploratory data +* `three_panel`: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -76,15 +76,15 @@ pre-configured layouts in You can also add [custom layouts](./api.md#customizing-the-layout) to your LIT instance by defining one or more `LitCanonicalLayout` instances and passing them -to the server. For an example, see `CUSTOM_LAYOUTS` in -[`lm_demo.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/lm_demo.py). +to the server. For an example, see +[`prompt_debugging/layouts.py`](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/layouts.py). Note: The pre-configured layouts are added to every `LitApp` instance using [dictionary updates](https://docs.python.org/3/library/stdtypes.html#dict) where the Mapping passed to the `LitApp` constructor overrides the pre-configured layouts `Mapping`. Thus, you can remove or change these pre-configured layouts as you like by passing a `Mapping` where the values of `simple`, `default`, -and/or `experimental` is `None` (to remove) or a `LitCanonicalLayout` instance +and/or `three_panel` is `None` (to remove) or a `LitCanonicalLayout` instance (to override) as you desire. The actual layout of components in the LIT UI, see diff --git a/docs/documentation/_sources/getting_started.md.txt b/docs/documentation/_sources/getting_started.md.txt index 80e4eae9..0ca605bf 100644 --- a/docs/documentation/_sources/getting_started.md.txt +++ b/docs/documentation/_sources/getting_started.md.txt @@ -1,6 +1,6 @@ # Getting Started with LIT - + @@ -72,8 +72,7 @@ variety of model types. The code for these is under [examples](https://github.co ; each is a small script that loads one or more models and starts a LIT server. -Most demos can be run with a single blaze command. To run the default one, you -can do: +Most demos can be run with a single command. To run the default one, you can do: ```sh python -m lit_nlp.examples.glue.demo \ diff --git a/docs/documentation/_sources/index.md.txt b/docs/documentation/_sources/index.md.txt index 211c7321..64921be0 100644 --- a/docs/documentation/_sources/index.md.txt +++ b/docs/documentation/_sources/index.md.txt @@ -1,6 +1,6 @@ # Learning Interpretability Tool (LIT) - + diff --git a/docs/documentation/_sources/ui_guide.md.txt b/docs/documentation/_sources/ui_guide.md.txt index 4a38a88c..bb5028ae 100644 --- a/docs/documentation/_sources/ui_guide.md.txt +++ b/docs/documentation/_sources/ui_guide.md.txt @@ -1,6 +1,6 @@ # UI Guide - + This is a user guide for the Learning Interpretability Tool (LIT) UI. @@ -42,7 +42,7 @@ LIT provides three pre-configured layouts: * `default`: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and -* `experimental`: A three-panel layout that puts exploratory data +* `three_panel`: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -130,19 +130,26 @@ controls and information. The left side of the toolbar contains a set of menus for quickly controlling datapoint selection and coloring. This includes the following controls: -* The **"Select related"** option adds any datapoints "related" to the current - selection. In LIT, "related" is defined as datapoints created from some - source datapoint (through manual editing or a datapoint generator), or a - source datapoint that a selected datapoint was created from. -* The **"Clear selection"** button deselects all selected datapoints. The - dropdown also contains a color legend for the current color setting. -* The **Slices** option allows quick selection of an already-created slice of - datapoints. -* The **"Datapoint color"** menu enables setting of the color of each - datapoint in the modules that visualize all datapoints (such as the - *Embeddings* and *Scalars* modules) by any number of datapoint features or - model outputs on those datapoints (such as coloring by some categorical - input feature, or by prediction error for a regression task). +* The **"Select datapoint"** menu provides a drop-down of several options: + * the **"Random"** option selects a random datapoint, + * the **"All related"** option adds any datapoints "related" to the + current selection. In LIT, "related" is defined as datapoints created + from some source datapoint (through manual editing or a datapoint + generator), or a source datapoint that a selected datapoint was created + from, + * the **"Parents"** option adds the source datapoints that the selected + datapoints were created from, + * the **"Children"** option adds the datapoints created from the selected + datapoints (through manual editing or a datapoint generator), + * the **Slices** option allows quick selection of an already-created slice + of datapoints, + * the **"Clear selection"** button deselects all selected datapoints. +* The **"Color by"** menu enables setting of the color of each datapoint in + the modules that visualize all datapoints (such as the *Embeddings* and + *Scalars* modules) by any number of datapoint features or model outputs on + those datapoints (such as coloring by some categorical input feature, or by + prediction error for a regression task). +* The **Slices** menu allows adding/selecting/removing slices of datapoints. Next to the menus is a button for pinning/unpinning a datapoint. Pinning a datapoint puts LIT into datapoint comparison mode, where two datapoints can be @@ -322,9 +329,9 @@ such as model predictions, enable them from the "Columns" dropdown. The datapoint editor shows the details of the primary selected datapoint, if one is selected. Any field can be edited, and a new datapoint created with those -edits through the **"Make new datapoint"** button. Any edit to an existing -datapoint must be saved as a new datapoint to be explored, to keep datapoints -immutable for simplicity of use. +edits through the **"Add"** button. Any edit to an existing datapoint must be +saved as a new datapoint to be explored, to keep datapoints immutable for +simplicity of use. When no datapoint is selected, the editor shows a blank datapoint that can be filled out by hand to create a completely new datapoint. @@ -473,24 +480,15 @@ background of each text piece is colored by the salience of that piece on the prediction, and hovering on any piece will display the exact value calculated for that piece. -There is an **"autorun"** button by each methodology. If it is checked, then -that calculation is made when a new primary datapoint is selected. If it is +There is an **"autorun"** button by each methodology on the right side of the +bar (the methodoloy name is on the left side). If it is checked, then that +calculation is made when a new primary datapoint is selected. If it is unchecked, the calculation isn't made until it is checked. This can be valuable so that expensive, long-running saliency calculations (such as LIME) aren't performed on every datapoint selection, but only when explicitly asked for. ![LIT saliency maps](./images/lit-salience.png "LIT saliency maps") -### Attention - -For models that return attention head information along with predictions, the -attention module shows which tokens are attended to between layers of a model. -Dropdowns allow you to select which layer and attention head is being -visualized. Line opacity is controlled by the magnitude of the attention between -those tokens. - -![LIT attention](./images/lit-attention.png "LIT attention"){w=500px align=center} - ## User Journeys In this section, we explore some example user journeys and how LIT enables them. @@ -515,51 +513,11 @@ model. ![Sentiment analysis](./images/lit-sentiment-analysis.png "Sentiment analysis") -### Gender Bias in Coreference - -Does a system encode gendered associations, which might lead to incorrect -predictions? We load a coreference model trained on OntoNotes, and load the -Winogender dataset into LIT for evaluation. Each Winogender example has a -pronoun and two candidate referents, one a occupation term like (“technician”) -and one is an “other participant” (like “customer”). Our model predicts -coreference probabilities for each candidate. We can explore the model’s -sensitivity to pronouns by comparing two examples side-by-side (see structured -predictions in screenshot). We can see how commonly the model makes similar -errors by paging through the dataset, or by selecting specific slices of -interest. - -![Winogender analysis](./images/lit-winogender.png "Winogender analysis") - -In the *Metrics* table, we can slice this selection by pronoun type and by the -true referent. On the set of male-dominated occupations (< 25% female by BLS), -we see the model performs well when the ground-truth agrees with the -stereotype - e.g. when the answer is the occupation term, male pronouns are -correctly resolved 83% of the time, compared to female pronouns only 37.5% of -the time (screenshot section (c)). - -![Winogender analysis](./images/lit-winogender-metrics.png "Winogender analysis") - -### Debugging Text Generation - - - -Does the training data explain a particular error in text generation? We analyze -an older T5 model on the CNN-DM summarization task. LIT’s *Scalars* module -allows us to look at per-example ROUGE scores, and quickly select an example -with middling performance (screenshot section (a)). We find the generated text -(screenshot section (b)) contains an erroneous constituent: “alastair cook was -replaced as captain by former captain ...”. We can dig deeper, using LIT’s -language modeling module (screenshot section (c)) to see that the token “by” is -predicted with high probability (28.7%). - -To find out how T5 arrived at this prediction, we utilize the “similarity -searcher” component through the datapoint generator (screenshot section (d)). -This performs a fast approximate nearest-neighbor lookup from a pre-built index -over the training corpus, using embeddings from the T5 decoder. With one click, -we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection. -We see that the words “captain” and “former” appear 34 and 16 times in these -examples–along with 3 occurrences of “replaced by” (screenshot section (e)), -suggesting a strong prior toward our erroneous phrase. - -![LIT sequence-to-sequence analysis](./images/lit-s2s-journey.png "LIT sequence-to-sequence analysis"){w=500px align=center} +### Sequence salience + +Sequence salience generalizes token-based salience to text-to-text models, +allowing you to explain the impact of the prompt tokens on parts of the model +output. + +Check out [here](components.md#sequence-salience) for more details on how to +navigate the Sequence Salience UI module. diff --git a/docs/documentation/_static/basic.css b/docs/documentation/_static/basic.css index 30fee9d0..7ebbd6d0 100644 --- a/docs/documentation/_static/basic.css +++ b/docs/documentation/_static/basic.css @@ -1,12 +1,5 @@ /* - * basic.css - * ~~~~~~~~~ - * * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ /* -- main layout ----------------------------------------------------------- */ @@ -115,15 +108,11 @@ img { /* -- search page ----------------------------------------------------------- */ ul.search { - margin: 10px 0 0 20px; - padding: 0; + margin-top: 10px; } ul.search li { - padding: 5px 0 5px 20px; - background-image: url(file.png); - background-repeat: no-repeat; - background-position: 0 7px; + padding: 5px 0; } ul.search li a { diff --git a/docs/documentation/_static/doctools.js b/docs/documentation/_static/doctools.js index d06a71d7..0398ebb9 100644 --- a/docs/documentation/_static/doctools.js +++ b/docs/documentation/_static/doctools.js @@ -1,12 +1,5 @@ /* - * doctools.js - * ~~~~~~~~~~~ - * * Base JavaScript utilities for all Sphinx HTML documentation. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ "use strict"; diff --git a/docs/documentation/_static/language_data.js b/docs/documentation/_static/language_data.js index 250f5665..c7fe6c6f 100644 --- a/docs/documentation/_static/language_data.js +++ b/docs/documentation/_static/language_data.js @@ -1,19 +1,12 @@ /* - * language_data.js - * ~~~~~~~~~~~~~~~~ - * * This script contains the language-specific data used by searchtools.js, * namely the list of stopwords, stemmer, scorer and splitter. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; -/* Non-minified version is copied as a separate JS file, is available */ +/* Non-minified version is copied as a separate JS file, if available */ /** * Porter Stemmer diff --git a/docs/documentation/_static/pygments.css b/docs/documentation/_static/pygments.css index c2e07c71..02b4b128 100644 --- a/docs/documentation/_static/pygments.css +++ b/docs/documentation/_static/pygments.css @@ -106,17 +106,17 @@ body[data-theme="dark"] .highlight .cp { color: #ff3a3a; font-weight: bold } /* body[data-theme="dark"] .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */ body[data-theme="dark"] .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */ body[data-theme="dark"] .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */ -body[data-theme="dark"] .highlight .gd { color: #d22323 } /* Generic.Deleted */ +body[data-theme="dark"] .highlight .gd { color: #ff3a3a } /* Generic.Deleted */ body[data-theme="dark"] .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */ body[data-theme="dark"] .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */ -body[data-theme="dark"] .highlight .gr { color: #d22323 } /* Generic.Error */ +body[data-theme="dark"] .highlight .gr { color: #ff3a3a } /* Generic.Error */ body[data-theme="dark"] .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */ body[data-theme="dark"] .highlight .gi { color: #589819 } /* Generic.Inserted */ body[data-theme="dark"] .highlight .go { color: #cccccc } /* Generic.Output */ body[data-theme="dark"] .highlight .gp { color: #aaaaaa } /* Generic.Prompt */ body[data-theme="dark"] .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */ body[data-theme="dark"] .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */ -body[data-theme="dark"] .highlight .gt { color: #d22323 } /* Generic.Traceback */ +body[data-theme="dark"] .highlight .gt { color: #ff3a3a } /* Generic.Traceback */ body[data-theme="dark"] .highlight .kc { color: #6ebf26; font-weight: bold } /* Keyword.Constant */ body[data-theme="dark"] .highlight .kd { color: #6ebf26; font-weight: bold } /* Keyword.Declaration */ body[data-theme="dark"] .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */ @@ -192,17 +192,17 @@ body:not([data-theme="light"]) .highlight .cp { color: #ff3a3a; font-weight: bol body:not([data-theme="light"]) .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */ body:not([data-theme="light"]) .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */ body:not([data-theme="light"]) .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */ -body:not([data-theme="light"]) .highlight .gd { color: #d22323 } /* Generic.Deleted */ +body:not([data-theme="light"]) .highlight .gd { color: #ff3a3a } /* Generic.Deleted */ body:not([data-theme="light"]) .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */ body:not([data-theme="light"]) .highlight .ges { color: #d0d0d0; font-weight: bold; font-style: italic } /* Generic.EmphStrong */ -body:not([data-theme="light"]) .highlight .gr { color: #d22323 } /* Generic.Error */ +body:not([data-theme="light"]) .highlight .gr { color: #ff3a3a } /* Generic.Error */ body:not([data-theme="light"]) .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */ body:not([data-theme="light"]) .highlight .gi { color: #589819 } /* Generic.Inserted */ body:not([data-theme="light"]) .highlight .go { color: #cccccc } /* Generic.Output */ body:not([data-theme="light"]) .highlight .gp { color: #aaaaaa } /* Generic.Prompt */ body:not([data-theme="light"]) .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */ body:not([data-theme="light"]) .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */ -body:not([data-theme="light"]) .highlight .gt { color: #d22323 } /* Generic.Traceback */ +body:not([data-theme="light"]) .highlight .gt { color: #ff3a3a } /* Generic.Traceback */ body:not([data-theme="light"]) .highlight .kc { color: #6ebf26; 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false,\n nestedClass: \"active\",\n\n // Offset & reflow\n offset: 0,\n reflow: false,\n\n // Event support\n events: true,\n };\n\n //\n // Methods\n //\n\n /**\n * Merge two or more objects together.\n * @param {Object} objects The objects to merge together\n * @returns {Object} Merged values of defaults and options\n */\n var extend = function () {\n var merged = {};\n Array.prototype.forEach.call(arguments, function (obj) {\n for (var key in obj) {\n if (!obj.hasOwnProperty(key)) return;\n merged[key] = obj[key];\n }\n });\n return merged;\n };\n\n /**\n * Emit a custom event\n * @param {String} type The event type\n * @param {Node} elem The element to attach the event to\n * @param {Object} detail Any details to pass along with the event\n */\n var emitEvent = function (type, elem, detail) {\n // Make sure events are enabled\n if (!detail.settings.events) return;\n\n // Create a new event\n var event = new CustomEvent(type, {\n bubbles: true,\n cancelable: true,\n detail: detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the 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function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n 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detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the calculations\n */\n var getOffset = function (settings) {\n // if the offset is a function run it\n if (typeof settings.offset === \"function\") {\n return parseFloat(settings.offset());\n }\n\n // Otherwise, return it as-is\n return parseFloat(settings.offset);\n };\n\n /**\n * Get the document element's height\n * @private\n * @returns {Number}\n */\n var getDocumentHeight = function () {\n return Math.max(\n document.body.scrollHeight,\n document.documentElement.scrollHeight,\n document.body.offsetHeight,\n document.documentElement.offsetHeight,\n document.body.clientHeight,\n document.documentElement.clientHeight,\n );\n };\n\n /**\n * Determine if an element is in view\n * @param {Node} elem The element\n * @param {Object} settings The settings for this instantiation\n * @param {Boolean} bottom If true, check if element is above bottom of viewport instead\n * @return {Boolean} Returns true if element is in the viewport\n */\n var isInView = function (elem, settings, bottom) {\n var bounds = elem.getBoundingClientRect();\n var offset = getOffset(settings);\n if (bottom) {\n return (\n parseInt(bounds.bottom, 10) <\n (window.innerHeight || document.documentElement.clientHeight)\n );\n }\n return parseInt(bounds.top, 10) <= offset;\n };\n\n /**\n * Check if at the bottom of the viewport\n * @return {Boolean} If true, page is at the bottom of the viewport\n */\n var isAtBottom = function () {\n if (\n Math.ceil(window.innerHeight + window.pageYOffset) >=\n getDocumentHeight()\n )\n return true;\n return false;\n };\n\n /**\n * Check if the last item should be used (even if not at the top of the page)\n * @param {Object} item The last item\n * @param {Object} settings The settings for this instantiation\n * @return {Boolean} If true, use the last item\n */\n var useLastItem = function (item, settings) {\n if (isAtBottom() && isInView(item.content, settings, true)) return true;\n return false;\n };\n\n /**\n * Get the active content\n * @param {Array} contents The content areas\n * @param {Object} settings The settings for this instantiation\n * @return {Object} The content area and matching navigation link\n */\n var getActive = function (contents, settings) {\n var last = contents[contents.length - 1];\n if (useLastItem(last, settings)) return last;\n for (var i = contents.length - 1; i >= 0; i--) {\n if (isInView(contents[i].content, settings)) return contents[i];\n }\n };\n\n /**\n * Deactivate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var deactivateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested || !nav.parentNode) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Remove the active class\n li.classList.remove(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n deactivateNested(li, settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n contents.push({\n nav: item,\n content: content,\n });\n });\n\n // Sort contents by the order they appear in the DOM\n sortContents(contents);\n };\n\n /**\n * Detect which content is currently active\n */\n publicAPIs.detect = function () {\n // Get the active content\n var active = getActive(contents, settings);\n\n // if there's no active content, deactivate and bail\n if (!active) {\n if (current) {\n deactivate(current, settings);\n current = null;\n }\n return;\n }\n\n // If the active content is the one currently active, do nothing\n if (current && active.content === current.content) return;\n\n // Deactivate the current content and activate the new content\n deactivate(current, settings);\n activate(active, settings);\n\n // Update the currently active content\n current = active;\n };\n\n /**\n * Detect the active content on scroll\n * Debounced for performance\n */\n var scrollHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(publicAPIs.detect);\n };\n\n /**\n * Update content sorting on resize\n * Debounced for performance\n */\n var resizeHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(function () {\n sortContents(contents);\n publicAPIs.detect();\n });\n };\n\n /**\n * Destroy the current instantiation\n */\n publicAPIs.destroy = function () {\n // Undo DOM changes\n if (current) {\n deactivate(current, settings);\n }\n\n // Remove event listeners\n window.removeEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.removeEventListener(\"resize\", resizeHandler, false);\n }\n\n // Reset variables\n contents = null;\n navItems = null;\n current = null;\n timeout = null;\n settings = null;\n };\n\n /**\n * Initialize the current instantiation\n */\n var init = function () {\n // Merge user options into defaults\n settings = extend(defaults, options || {});\n\n // Setup variables based on the current DOM\n publicAPIs.setup();\n\n // Find the currently active content\n publicAPIs.detect();\n\n // Setup event listeners\n window.addEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.addEventListener(\"resize\", resizeHandler, false);\n }\n };\n\n //\n // Initialize and return the public APIs\n //\n\n init();\n return publicAPIs;\n };\n\n //\n // Return the Constructor\n //\n\n return Constructor;\n },\n);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader(positionY) {\n const headerTop = Math.floor(header.getBoundingClientRect().top);\n\n console.log(`headerTop: ${headerTop}`);\n if (headerTop == 0 && positionY != headerTop) {\n header.classList.add(\"scrolled\");\n } else {\n header.classList.remove(\"scrolled\");\n }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n if (positionY < GO_TO_TOP_OFFSET) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n } else {\n if (positionY < lastScrollTop) {\n document.documentElement.classList.add(\"show-back-to-top\");\n } else if (positionY > lastScrollTop) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n }\n }\n lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n if (tocScroll === null) {\n return;\n }\n\n // top of page.\n if (positionY == 0) {\n tocScroll.scrollTo(0, 0);\n } else if (\n // bottom of page.\n Math.ceil(positionY) >=\n Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n ) {\n tocScroll.scrollTo(0, tocScroll.scrollHeight);\n } else {\n // somewhere in the middle.\n const current = document.querySelector(\".scroll-current\");\n if (current == null) {\n return;\n }\n\n // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n // // scroll the currently \"active\" heading in toc, into view.\n // const rect = current.getBoundingClientRect();\n // if (0 > rect.top) {\n // current.scrollIntoView(true); // the argument is \"alignTop\"\n // } else if (rect.bottom > window.innerHeight) {\n // current.scrollIntoView(false);\n // }\n }\n}\n\nfunction scrollHandler(positionY) {\n scrollHandlerForHeader(positionY);\n scrollHandlerForBackToTop(positionY);\n scrollHandlerForTOC(positionY);\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Theme Toggle\n////////////////////////////////////////////////////////////////////////////////\nfunction setTheme(mode) {\n if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n mode = \"auto\";\n }\n\n document.body.dataset.theme = mode;\n localStorage.setItem(\"theme\", mode);\n console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n if (prefersDark) {\n // Auto (dark) -> Light -> Dark\n if (currentTheme === \"auto\") {\n setTheme(\"light\");\n } else if (currentTheme == \"light\") {\n setTheme(\"dark\");\n } else {\n setTheme(\"auto\");\n }\n } else {\n // Auto (light) -> Dark -> Light\n if (currentTheme === \"auto\") {\n setTheme(\"dark\");\n } else if (currentTheme == \"dark\") {\n setTheme(\"light\");\n } else {\n setTheme(\"auto\");\n }\n }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n let last_known_scroll_position = 0;\n let ticking = false;\n\n window.addEventListener(\"scroll\", function (e) {\n last_known_scroll_position = window.scrollY;\n\n if (!ticking) {\n window.requestAnimationFrame(function () {\n scrollHandler(last_known_scroll_position);\n ticking = false;\n });\n\n ticking = true;\n }\n });\n window.scroll();\n}\n\nfunction setupScrollSpy() {\n if (tocScroll === null) {\n return;\n }\n\n // Scrollspy -- highlight table on contents, based on scroll\n new Gumshoe(\".toc-tree a\", {\n reflow: true,\n recursive: true,\n navClass: \"scroll-current\",\n offset: () => {\n let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n return header.getBoundingClientRect().height + 2.5 * rem + 1;\n },\n });\n}\n\nfunction setupTheme() {\n // Attach event handlers for toggling themes\n const buttons = document.getElementsByClassName(\"theme-toggle\");\n Array.from(buttons).forEach((btn) => {\n btn.addEventListener(\"click\", cycleThemeOnce);\n });\n}\n\nfunction setup() {\n setupTheme();\n setupScrollHandler();\n setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n document.body.parentNode.classList.remove(\"no-js\");\n\n header = document.querySelector(\"header\");\n tocScroll = document.querySelector(\".toc-scroll\");\n\n setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","GO_TO_TOP_OFFSET","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","headerTop","floor","scrollHandlerForHeader","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""} \ No newline at end of file diff --git a/docs/documentation/_static/searchtools.js b/docs/documentation/_static/searchtools.js index 7918c3fa..2c774d17 100644 --- a/docs/documentation/_static/searchtools.js +++ b/docs/documentation/_static/searchtools.js @@ -1,12 +1,5 @@ /* - * searchtools.js - * ~~~~~~~~~~~~~~~~ - * * Sphinx JavaScript utilities for the full-text search. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * */ "use strict"; @@ -20,7 +13,7 @@ if (typeof Scorer === "undefined") { // and returns the new score. /* score: result => { - const [docname, title, anchor, descr, score, filename] = result + const [docname, title, anchor, descr, score, filename, kind] = result return score }, */ @@ -47,6 +40,14 @@ if (typeof Scorer === "undefined") { }; } +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + const _removeChildren = (element) => { while (element && element.lastChild) element.removeChild(element.lastChild); }; @@ -64,9 +65,13 @@ const _displayItem = (item, searchTerms, highlightTerms) => { const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; const contentRoot = document.documentElement.dataset.content_root; - const [docName, title, anchor, descr, score, _filename] = item; + const [docName, title, anchor, descr, score, _filename, kind] = item; let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); let requestUrl; let linkUrl; if (docBuilder === "dirhtml") { @@ -99,7 +104,7 @@ const _displayItem = (item, searchTerms, highlightTerms) => { .then((data) => { if (data) listItem.appendChild( - Search.makeSearchSummary(data, searchTerms) + Search.makeSearchSummary(data, searchTerms, anchor) ); // highlight search terms in the summary if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js @@ -115,9 +120,11 @@ const _finishSearch = (resultCount) => { "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." ); else - Search.status.innerText = _( - `Search finished, found ${resultCount} page(s) matching the search query.` - ); + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${resultCount}', resultCount); }; const _displayNextItem = ( results, @@ -137,6 +144,22 @@ const _displayNextItem = ( // search finished, update title and status message else _finishSearch(resultCount); }; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; /** * Default splitQuery function. Can be overridden in ``sphinx.search`` with a @@ -160,13 +183,26 @@ const Search = { _queued_query: null, _pulse_status: -1, - htmlToText: (htmlString) => { + htmlToText: (htmlString, anchor) => { const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); - htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent !== undefined) return docContent.textContent; + if (docContent) return docContent.textContent; + console.warn( - "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." ); return ""; }, @@ -219,6 +255,7 @@ const Search = { searchSummary.classList.add("search-summary"); searchSummary.innerText = ""; const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); searchList.classList.add("search"); const out = document.getElementById("search-results"); @@ -239,16 +276,7 @@ const Search = { else Search.deferQuery(query); }, - /** - * execute search (requires search index to be loaded) - */ - query: (query) => { - const filenames = Search._index.filenames; - const docNames = Search._index.docnames; - const titles = Search._index.titles; - const allTitles = Search._index.alltitles; - const indexEntries = Search._index.indexentries; - + _parseQuery: (query) => { // stem the search terms and add them to the correct list const stemmer = new Stemmer(); const searchTerms = new Set(); @@ -284,22 +312,40 @@ const Search = { // console.info("required: ", [...searchTerms]); // console.info("excluded: ", [...excludedTerms]); - // array of [docname, title, anchor, descr, score, filename] - let results = []; + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + const normalResults = []; + const nonMainIndexResults = []; + _removeChildren(document.getElementById("search-progress")); - const queryLower = query.toLowerCase(); + const queryLower = query.toLowerCase().trim(); for (const [title, foundTitles] of Object.entries(allTitles)) { - if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { for (const [file, id] of foundTitles) { - let score = Math.round(100 * queryLower.length / title.length) - results.push([ + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ docNames[file], titles[file] !== title ? `${titles[file]} > ${title}` : title, id !== null ? "#" + id : "", null, - score, + score + boost, filenames[file], + SearchResultKind.title, ]); } } @@ -308,46 +354,48 @@ const Search = { // search for explicit entries in index directives for (const [entry, foundEntries] of Object.entries(indexEntries)) { if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { - for (const [file, id] of foundEntries) { - let score = Math.round(100 * queryLower.length / entry.length) - results.push([ + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ docNames[file], titles[file], id ? "#" + id : "", null, score, filenames[file], - ]); + SearchResultKind.index, + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } } } } // lookup as object objectTerms.forEach((term) => - results.push(...Search.performObjectSearch(term, objectTerms)) + normalResults.push(...Search.performObjectSearch(term, objectTerms)) ); // lookup as search terms in fulltext - results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); // let the scorer override scores with a custom scoring function - if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); - - // now sort the results by score (in opposite order of appearance, since the - // display function below uses pop() to retrieve items) and then - // alphabetically - results.sort((a, b) => { - const leftScore = a[4]; - const rightScore = b[4]; - if (leftScore === rightScore) { - // same score: sort alphabetically - const leftTitle = a[1].toLowerCase(); - const rightTitle = b[1].toLowerCase(); - if (leftTitle === rightTitle) return 0; - return leftTitle > rightTitle ? -1 : 1; // inverted is intentional - } - return leftScore > rightScore ? 1 : -1; - }); + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; // remove duplicate search results // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept @@ -361,7 +409,12 @@ const Search = { return acc; }, []); - results = results.reverse(); + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); // for debugging //Search.lastresults = results.slice(); // a copy @@ -432,6 +485,7 @@ const Search = { descr, score, filenames[match[0]], + SearchResultKind.object, ]); }; Object.keys(objects).forEach((prefix) => @@ -466,14 +520,18 @@ const Search = { // add support for partial matches if (word.length > 2) { const escapedWord = _escapeRegExp(word); - Object.keys(terms).forEach((term) => { - if (term.match(escapedWord) && !terms[word]) - arr.push({ files: terms[term], score: Scorer.partialTerm }); - }); - Object.keys(titleTerms).forEach((term) => { - if (term.match(escapedWord) && !titleTerms[word]) - arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); - }); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } } // no match but word was a required one @@ -496,9 +554,8 @@ const Search = { // create the mapping files.forEach((file) => { - if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) - fileMap.get(file).push(word); - else fileMap.set(file, [word]); + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); }); }); @@ -539,6 +596,7 @@ const Search = { null, score, filenames[file], + SearchResultKind.text, ]); } return results; @@ -549,8 +607,8 @@ const Search = { * search summary for a given text. keywords is a list * of stemmed words. */ - makeSearchSummary: (htmlText, keywords) => { - const text = Search.htmlToText(htmlText); + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); if (text === "") return null; const textLower = text.toLowerCase(); diff --git a/docs/documentation/_static/styles/furo-extensions.css b/docs/documentation/_static/styles/furo-extensions.css index bc447f22..82295876 100644 --- a/docs/documentation/_static/styles/furo-extensions.css +++ b/docs/documentation/_static/styles/furo-extensions.css @@ -1,2 +1,2 @@ -#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar 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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto\n display: block\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #646776; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2962ff;\n --color-brand-content: #2a5adf;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link-underline--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #ffffffcc; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2b8cee;\n --color-brand-content: #368ce2;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: 1rem\n width: 1rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1rem\n width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page svg\n color: inherit\n height: 1rem\n width: 1rem\n\n.sidebar-toggle\n position: absolute\n display: none\n// \n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// \n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n .theme-toggle-header\n display: block\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: 1.25rem\n width: 1.25rem\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: var(--header-height)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Center the page, and accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n .content\n margin-left: auto\n margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n .content\n padding: 0 $content-padding--small\n // Don't float sidebars to the right.\n article aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 3.5rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\n.versionmodified\n font-style: italic\ndiv.versionadded, div.versionchanged, div.deprecated\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > p,\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 0.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(0.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(0.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the

+Skip to content +
@@ -130,7 +173,8 @@
@@ -186,11 +230,17 @@ Back to top
- +
@@ -200,13 +250,13 @@
-
+
-

LIT Python API#

- +

LIT Python API

+
-

Design Overview#

+

Design Overview

LIT is a modular system, comprising a collection of backend components (written in Python) and frontend modules (written in TypeScript). Most users will develop against the Python API, which is documented below and allows LIT to be extended @@ -223,7 +273,7 @@

Design OverviewLit[1] for modularity and MobX for +Lit[1] for modularity and MobX for state management. It consists of a core UI framework, a set of shared “services” which manage persistent state, and a set of independent modules which render visualizations and support user interaction. For more details, see the @@ -231,7 +281,7 @@

Design Overviewfrontend developer guide.

-

Adding Models and Data#

+

Adding Models and Data

To run LIT with your own models and data, you can create a custom demo.py script that passes these to the LIT server. For example:

def main(_):
@@ -261,7 +311,7 @@ 

Adding Models and DataFor pre-built demo.py examples, check out https://github.com/PAIR-code/lit/tree/main/lit_nlp/examples

-

Validating Models and Data#

+

Validating Models and Data

Datasets and models can optionally be validated by LIT to ensure that dataset examples match their spec and that model output values match their spec. This can be very helpful during development of new model and dataset wrappers @@ -279,7 +329,7 @@

Validating Models and Data -

Datasets#

+

Datasets

Datasets (Dataset) are just a list of examples, with associated type information following LIT’s type system.

@@ -341,7 +391,7 @@

Datasets these features for slicing, binning, etc., and they will also be available to interpretation components such as custom metrics.

-

Transformations#

+

Transformations

The Dataset class also supports a limited set of transformations, similar to TensorFlow’s tf.data.Dataset @@ -356,11 +406,11 @@

Transformations"document" field can be used with a model expecting -a "text" input via Dataset.remap({"document": "text"}).[2]

+a "text" input via Dataset.remap({"document": "text"}).[2]

-

Models#

+

Models

Models (Model) are functions which take inputs and produce outputs, with associated type information following LIT’s type system. The core API consists @@ -422,7 +472,7 @@

Models#If your model is on a remote server, consider using the BatchedRemoteModel base class, which implements parallel batched requests using a thread pool.

-

Adding more outputs#

+

Adding more outputs

The above example defined a black-box model, with predictions but no access to internals. If we want a richer view into the model’s behavior, we can add additional return fields corresponding to hidden-state activations, gradients, @@ -471,7 +521,7 @@

Adding more outputs -

Optional inputs#

+

Optional inputs

By default, LIT treats input_spec fields as required. However, this can be set to false if you wish to define optional model inputs. For example, a model that can accept pre-tokenized inputs might have the following spec:

@@ -495,21 +545,21 @@

Optional inputs -

Interpretation Components#

+

Interpretation Components

Backend interpretation components include metrics, salience maps, visualization aids like UMAP, and counterfactual generator plug-ins.

Most such components implement the Interpreter API. Conceptually, this is any function that takes a set of datapoints and a model, -and produces some output.[3] For example, +and produces some output.[3] For example, local gradient-based salience (GradientNorm) processes the TokenGradients and Tokens returned by a model and produces a list of scores for each token. The Integrated Gradients saliency method additionally requires a TokenEmbeddings input and corresponding output, as well as a label field Target to pin the gradient target to the same class as an input and corresponding output. See the -GLUE models class +GLUE models class for an example of these spec requirements.

The core API involves implementing the run() method:

  def run(self,
@@ -568,7 +618,7 @@ 

Interpretation Components -

Metrics#

+

Metrics

For metrics, the SimpleMetrics class implements the spec-matching and input-unpacking logic to satisfy the @@ -602,7 +652,7 @@

Metrics#< on the unpacked values.

-

Generators#

+

Generators

Conceptually, a generator is just an interpreter that returns new input examples. These may depend on the input only, as for techniques such as back- translation, or can involve feedback from the model, such as for adversarial @@ -638,7 +688,7 @@

Generatorsconfig, such as the list of substitutions for the word replacer.

-

Backtranslator Generator#

+

Backtranslator Generator

The backtranslator generator translates text segment inputs into foreign languages and back to the source language in order to create paraphrases. @@ -652,7 +702,7 @@

Backtranslator Generator -

Configuration UI#

+

Configuration UI

Interpreter components support an optional config option to specify run-time options, such as the number of samples for LIME or the pivot languages for back-translation. LIT provides a simple DSL to define these options, which will @@ -708,7 +758,7 @@

Configuration UI -

Type System#

+

Type System

LIT passes data around (e.g., between the server and the web app) as flat records with string keys. In Python types these are Mapping[str, ...] and in TypeScript types these are {[key: string]: unknown}. LIT serializes these @@ -731,7 +781,7 @@

Type Systemtypes.py, with parallel implementations in lit_types.ts.

-

Conventions#

+

Conventions

LIT supports several different “kinds” of Specs (input vs output vs meta, etc.), and their use in context has specific implications, described per base class below.

@@ -747,7 +797,7 @@

Conventionslit_nlp.api.model.Model

+
  • lit_nlp.api.model.Model

    • .input_spec() -> Spec describes the shape required of all records passed into the Model.predict() function via the inputs argument. LIT @@ -762,7 +812,7 @@

      Conventionslit_nlp.api.components.[Interpreter | Generator]

      +
    • lit_nlp.api.components.[Interpreter | Generator]

      • .config_spec() -> Spec describes the user-configurable parameters for running this component. Returning an empty Spec means that this @@ -778,7 +828,7 @@

        Conventionslit_nlp.api.components.Metrics

        +
      • lit_nlp.api.components.Metrics

        • .config_spec() -> Spec describes the user-configurable parameters for running this component. Returning an empty Spec means that this @@ -818,7 +868,7 @@

          Conventionslanguage model +language model might output 'tokens': lit_types.Tokens(...), and accept as (optional) input 'tokens': lit_types.Tokens(required=False, ...). An interpretability component could take output from the former, swap one or more tokens (e.g. @@ -827,17 +877,17 @@

          Conventions -

          Compatibility Checks#

          +

          Compatibility Checks

          LIT’s type system plays a critical role in ensuring reliability of and interoperability between the Model, Dataset, Interpreter, Generator, and Metrics classes:

          • The Model-Dataset compatibility check ensures that the Model.input_spec() is a subset of the Dataset.spec(). The base -Model class provides a robust and universal implementation of +Model class provides a robust and universal implementation of this check in the is_compatible_with_dataset() API, but you can override this method in your Model subclass if you so choose.

          • -
          • All lit_nlp.api.components classes provide an +

          • All lit_nlp.api.components classes provide an is_compatible API to check their compatibility against Models and Datasets, as appropriate. For example, the WordReplacer generator only checks against the Dataset @@ -855,11 +905,9 @@

            Compatibility ChecksSpecs.

            When implementing your own LIT components and modules, you can use -utils.find_spec_keys() -(Python) and -findSpecKeys() -(TypeScript) to identify fields of interest in a Spec. These methods recognize -and respect subclasses. For example, +utils.find_spec_keys() (Python) and +findSpecKeys() (TypeScript) to identify fields of interest in a +Spec. These methods recognize and respect subclasses. For example, utils.find_spec_keys(spec, Scalar) will also match any RegressionScore fields, but utils.find_spec_keys(spec, RegressionScore) will not return all Scalar fields in the Spec.

            @@ -872,7 +920,7 @@

            Compatibility Checks

  • -

    An In-Depth Example#

    +

    An In-Depth Example

    Consider the following example from the MNLI demo. The MultiNLI dataset might define the following Spec.

    # Dataset.spec()
    @@ -938,9 +986,14 @@ 

    An In-Depth Exampleexamples for more.

    -

    Available types#

    -

    The full set of LitTypes is defined in types.py. Numeric types such as Integer and Scalar have predefined ranges that can be overridden using corresponding min_val and max_val attributes as seen here. The different types available in LIT are summarized -in the table below.

    +

    Available types

    +

    The full set of LitTypes is defined in +types.py. Numeric types +such as Integer and Scalar have predefined ranges that can be overridden +using corresponding min_val and max_val attributes as seen in +penguin data +INPUT_SPEC. The different types available in LIT are summarized in the table +below.

    Note: Bracket syntax, such as <float>[num_tokens], refers to the shapes of NumPy arrays where each element inside the brackets is an integer.

    @@ -1084,11 +1137,11 @@

    Available types -

    Server Configuration#

    +

    Server Configuration

    Some properties of the LIT frontend can be configured from Python as arguments to dev_server.Server(). These include:

      -
    • page_title: set a custom page title, such as “Coreference Demo”.

    • +
    • page_title: set a custom page title.

    • canonical_url: set a “canonical” URL (such as a shortlink) that will be used as the base when copying links from the LIT UI.

    • default_layout: set the default UI layout, by name. See layout.ts and @@ -1108,25 +1161,19 @@

      Server Configurationdescription() method which can be used to specify a human-readable description or help text that will appear in the UI.

      -

      Customizing the Layout#

      +

      Customizing the Layout

      You can specify custom web app layouts from Python via the layouts= attribute. The value should be a Mapping[str, LitCanonicalLayout], such as:

      -
      LM_LAYOUT = layout.LitCanonicalLayout(
      +
      PENGUIN_LAYOUT = layout.LitCanonicalLayout(
           upper={
      -        "Main": [
      -            modules.EmbeddingsModule,
      +        'Main': [
      +            modules.DiveModule,
                   modules.DataTableModule,
                   modules.DatapointEditorModule,
               ]
           },
      -    lower={
      -        "Predictions": [
      -            modules.LanguageModelPredictionModule,
      -            modules.ConfusionMatrixModule,
      -        ],
      -        "Counterfactuals": [modules.GeneratorModule],
      -    },
      -    description="Custom layout for language models.",
      +    lower=layout.STANDARD_LAYOUT.lower,
      +    description='Custom layout for the Palmer Penguins demo.',
       )
       
      @@ -1135,13 +1182,12 @@

      Customizing the Layoutmodels, datasets, # other args... - layouts={"lm": LM_LAYOUT}, + layouts=layout.DEFAULT_LAYOUTS | {'penguins': PENGUIN_LAYOUT}, + default_layout='penguins', **server_flags.get_flags()) return lit_demo.serve()

      -

      For a full example, see -lm_demo.py.

      You can see the pre-configured layouts provided by LIT, as well as the list of modules that can be included in your custom layout in layout.py. A @@ -1171,7 +1217,7 @@

      Customizing the Layout

      -

      Accessing the LIT UI in Notebooks#

      +

      Accessing the LIT UI in Notebooks

      As an alternative to running a LIT server and connecting to it through a web browser, LIT can be used directly inside of python notebook environments, such as Colab and @@ -1197,20 +1243,20 @@

      Accessing the LIT UI in Notebooksexample notebook.

      -

      Using LIT components outside of LIT#

      +

      Using LIT components outside of LIT

      All LIT Python components (models, datasets, interpreters, metrics, generators, etc.) are standalone classes that do not depend on the serving framework. You can easily use them from Colab, in scripts, or in your libraries. This can also be handy for development, as you can test new models or components without needing to reload the server or click the UI.

      For example, to view examples in a dataset:

      -
      from lit_nlp.examples.datasets import glue
      -dataset = glue.SST2Data('validation')
      +
      from lit_nlp.examples.glue import data as glue_data
      +dataset = glue_data.SST2Data('validation')
       print(dataset.examples)  # list of records {"sentence": ..., "label": ...}
       

      And to run inference on a few of them:

      -
      from lit_nlp.examples.models import glue_models
      +
      from lit_nlp.examples.glue import models as glue_models
       
       model = glue_models.SST2Model("/path/to/model/files")
       preds = list(model.predict(dataset.examples[:5]))
      @@ -1228,15 +1274,17 @@ 

      Using LIT components outside of LITLIT_components_example.ipynb.

      +

      +


    -

    @@ -1357,8 +1403,8 @@

    Using LIT components outside of LIT - + - + \ No newline at end of file diff --git a/docs/documentation/components.html b/docs/documentation/components.html index bfbf62a6..bf6def94 100644 --- a/docs/documentation/components.html +++ b/docs/documentation/components.html @@ -5,11 +5,11 @@ - + Components and Features - 🔥LIT 1.0 documentation - - + + @@ -69,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -113,6 +154,8 @@
    Hide table of contents sidebar
    +
    Skip to content +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    @@ -200,13 +250,13 @@
    -
    +
    -

    Components and Features#

    - +

    Components and Features

    +
    -

    Framework and Model Support#

    +

    Framework and Model Support

    LIT is framework-agnostic and is compatible with any model that can be wrapped in a Python class for inference. In particular, we’ve tested with TF1.x, TF2, JAX, and PyTorch, as well as models that use custom C++ inference code (wrapped @@ -219,7 +269,7 @@

    Framework and Model Supportmodel and dataset classes, but we also have out-of-the-box support for a few modeling frameworks, described below.

    -

    HuggingFace Transformers#

    +

    HuggingFace Transformers

    Many of the open-source LIT examples use HuggingFace Transformers via their TF2/Keras model classes. These give easy @@ -230,7 +280,7 @@

    HuggingFace Transformers -

    TF1.x Estimator#

    +

    TF1.x Estimator

    LIT supports Estimator and other TF1.x models, but the model wrappers can be more involved due to the need to explicitly manage the graph and sessions. (In particular: Estimator.predict() cannot be used because it reloads the model on @@ -242,13 +292,12 @@

    TF1.x EstimatorSavedModel and load this in an eager mode -runtime. This leads to much simpler code (see -this example), -but may require changes to your SavedModel exporter in order to access model -internals like embeddings, gradients, or attention.

    +runtime. This leads to much simpler code, but may require changes to your +SavedModel exporter in order to access model internals like embeddings, +gradients, or attention.

    -

    Remote or hosted models#

    +

    Remote or hosted models

    LIT can easily interact with models hosted via an RPC or HTTP endpoint, including Servomatic. In this usage, the model weights and computation remain on the server, while the LIT Model implementation simply manages the RPC stub and @@ -259,7 +308,7 @@

    Remote or hosted models

    -

    Static predictions#

    +

    Static predictions

    LIT works best when the model can be queried interactively, but this isn’t always possible for all situations. The StaticPredictions @@ -270,7 +319,7 @@

    Static predictions -

    Data loading#

    +

    Data loading

    LIT can load data from almost any format, including TFRecord, Capacitor, SSTable, or even SQL queries, via a custom Python class that implements the Dataset API. Many of our demos use TFDS, and the LIT loader @@ -283,7 +332,7 @@

    Data loading -

    Input and Output Types#

    +

    Input and Output Types

    LIT uses an extensible system of semantic types to describe data and models. This allows for flexible support of a number of different input and output modalities common to NLP and other domains. For a full reference, see the @@ -297,13 +346,11 @@

    Input and Output Types
    -

    Classification#

    +

    Classification

    LIT supports many features for classification tasks, including common metrics, confusion matrices, and custom thresholding via the UI. Classification is implemented with the MulticlassPreds and CategoryLabel types.

    -

    Regression / Scoring#

    +

    Regression / Scoring

    Regression or scoring models also are well-supported with metrics, bucketed faceting, and scatterplots of scalar output. Regression is implemented with the Scalar (input) and RegressionScore (output) types.

    @@ -336,17 +381,15 @@

    Regression / ScoringRegressionScore field should set parent= to the name of this field.

  • For an example, see the STS-B textual similarity task in -examples/glue_demo.py.

  • +examples/glue/demo.py.

    -

    Multi-label classification#

    +

    Multi-label classification

    LIT supports multi-label tasks, when a model can label a single example with more than one label. Multi-label classification is implemented with the SparseMultilabelPreds and SparseMultilabel types.

    Generated Text Module

    -

    Span Labeling and Structured Prediction#

    +

    Span Labeling and Structured Prediction

    LIT can support a variety of structured prediction types, and provides rich, interactive visualizations.

      @@ -404,7 +445,7 @@

      Span Labeling and Structured PredictionStructured Predictions Module

    -

    Multiple input segments#

    +

    Multiple input segments

    LIT can easily handle multiple text fields, or a mix of text, categorical, scalar, and other input features. LIT does not explicitly “privilege” one input field, and metadata in the model spec can be used to align gradients, attention, @@ -414,11 +455,11 @@

    Multiple input segmentsDataset class documentation and the corresponding Model.

  • For a more involved code example including per-token gradients, see -examples/glue_demo.py.

  • +examples/glue/demo.py.

    -

    Tabular data#

    +

    Tabular data

    LIT can be used as a replacement for the What-If Tool but with more extensibility, when working with predictions over tabular data.

    Some interpreters, such as Kernel SHAP, require models that use tabular data. In @@ -432,10 +473,10 @@

    Tabular dataSparseMultilabel) field.

    For a demo using a penguin stats dataset/binary classification task, see -examples/penguin_demo.py.

    +examples/penguin/demo.py.

    -

    Images#

    +

    Images

    LIT also contains support for models with images as input features or generated images as model output. The LIT type ImageBytes can be used as a feature in datasets and as part of an input spec or output spec for a model. That feature’s @@ -443,17 +484,10 @@

    Images#NOTE: We may transition images away from encoded strings, moving to individual pixel color values. We will ensure we don’t break existing checked-in code with such a change.

    -

    -

    Token-based Salience#

    +

    Token-based Salience

    LIT supports several methods for token-based input salience, including gradient-based methods as well as black-box techniques like LIME that don’t require any access to model internals. Output is rendered in the Salience Maps @@ -465,7 +499,7 @@

    Token-based SalienceRegressionScore) and generation (GeneratedText or GeneratedTextCandidates) outputs.

    -

    Gradient Norm#

    +

    Gradient Norm

    This is a simple method, in which salience scores are proportional to the L2 norm of the gradient, i.e. the score for token \(i\) is:

    @@ -490,7 +524,7 @@

    Gradient Norm -

    Gradient-dot-Input#

    +

    Gradient-dot-Input

    In this method, salience scores are proportional to the dot product of the input embeddings and their gradients, i.e. for token \(i\) we compute:

    @@ -499,8 +533,8 @@

    Gradient-dot-Input\(\hat{y}\), while a negative score suggests that the prediction would be stronger -if that token was removed.

    +\(\hat{y}\), while a negative score suggests that the prediction would be +stronger if that token was removed.

    To enable this method, your model should, as part of the output spec and predict() implementation:

    -

    LIME#

    +

    LIME

    LIME is a black-box salience method that does not require access to any model internals. It works by generating a set of perturbed inputs - generally, by dropping out or masking tokens - and training a @@ -578,7 +612,7 @@

    LIME#RegressionScore) model.

    -

    Target Selection on Classification Output#

    +

    Target Selection on Classification Output

    For all salience methods, we require that the class to explain is given as a label field in the input. For example, if the input example is:

    {"text": "this movie was terrible!", "label": "0"}
    @@ -615,7 +649,7 @@ 

    Target Selection on Classification Output -

    Sequence Salience#

    +

    Sequence Salience

    Sequence salience generalizes token-based salience to text-to-text models, allowing you to explain the impact of the prompt tokens on parts of the model output.

    @@ -625,7 +659,7 @@

    Sequence SalienceSequence salience - visualization

    The UI supports multiple options for analysis, including:

    -

    Salience Clustering#

    +

    Salience Clustering

    LIT includes a basic implementation of the salience clustering method from Ebert et al. 2022, which uses k-means on a salience-weighted bag-of-words representation to find patterns in model @@ -676,7 +710,7 @@

    Salience Clusteringthe paper.

    -

    Tabular Feature Attribution#

    +

    Tabular Feature Attribution

    Tabular feature attribution seeks to understand the importance of a column of data on a model’s predictions. LIT’s tabular feature attribution module supports this analysis using the SHAP interpreter. @@ -687,7 +721,7 @@

    Tabular Feature AttributionTabular feature attribution module module

    -

    Pixel-based Salience#

    +

    Pixel-based Salience

    LIT also supports pixel-based salience methods, for models that take images as inputs. Output is rendered in the Salience Maps module in the LIT UI, which allows for comparison of multiple methods at once.

    @@ -707,9 +741,7 @@

    Pixel-based Salience

    The model should be able to accept input images as numpy arrays in addition -to accepting base64 URL encoded format. See -mobilenet.py -for an example.

    +to accepting base64 URL encoded format.

    A variety of image saliency techniques are implemented for models that return @@ -720,33 +752,8 @@

    Pixel-based SalienceEach of these techniques returns a saliency map image as a base64-encoded string through the ImageSalience type.

    -
    -

    Attention#

    -

    LIT can display a visualization of attention heads from transformers and other -models:

    -

    Attention Visualization

    -

    To enable this, your model should return one or more fields of the type -AttentionHeads, with values as arrays of shape <float>[num_heads, num_tokens, num_tokens]. Each field represents a set of heads at a single layer of the -model, so models will often have more than one:

    -
       def output_spec(self) -> types.Spec:
    -     return {
    -         # ...
    -         "tokens": lit_types.Tokens(parent="input_text"),
    -         "layer_0/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
    -         "layer_1/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
    -         "layer_2/attention": lit_types.AttentionHeads(align_in="tokens", align_out="tokens"),
    -         # ...
    -     }
    -
    -
    -

    The align_in and align_out attributes are the names of Tokens fields for -the source and target tokens for that layer. For self-attention (such as in -BERT) these would name the same field, but for encoder-decoder attention they -could reference different token sets (such as “input_tokens” and -“target_tokens”).

    -
    -

    Embedding Projector#

    +

    Embedding Projector

    LIT includes a version of the embedding projector which can be used to visualize the latent space of your model, in order to find clusters or patterns @@ -762,16 +769,16 @@

    Embedding Projector<float>[emb_dim] for each example.

    -

    Aggregate Analysis#

    +

    Aggregate Analysis

    -

    Metrics#

    +

    Metrics

    LIT includes common metrics for classification, regression, and seq2seq (BLEU) by default, which will appear in the table when the appropriate types are present in the model output and input data. Metrics can be computed on the whole dataset, a selected subset, or on facets defined by particular features. For example, we could facet by class label:

    Metrics Table

    -

    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Performance” tab.

    +

    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Metrics” tab.

    To enable metrics, your model should set the parent attribute on one or more output fields, pointing to the name of the input field that it should be evaluated against. For example, for classification, the data spec might have:

    @@ -796,19 +803,19 @@

    Metrics#< API documentation for more.

    -

    Confusion Matrix#

    +

    Confusion Matrix

    LIT includes a powerful and flexible confusion matrix, which can be used to compare predictions to gold labels as well as to compare between two models or between different categorical features. You can click cells or row/column headers to select a subset of examples, which is useful for intersectional analysis.

    Confusion Matrix

    -

    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Performance” tab.

    +

    To try this out, see https://pair-code.github.io/lit/demos/glue.html and navigate to the “Metrics” tab.

    The confusion matrix is supported for classification models, or if the input data includes any categorical features (CategoryLabel).

    -

    Scalar Plots#

    +

    Scalar Plots

    LIT includes scatterplots for scalar features, including plain scalars (Scalar or RegressionScore) as well as per-class probabilities from classification output (MulticlassPreds).

    @@ -820,7 +827,7 @@

    Scalar Plotshttps://pair-code.github.io/lit/demos/glue.html and navigate to the “Predictions” tab.

    -

    Binary Classification Thresholds#

    +

    Binary Classification Thresholds

    For binary classification models, LIT contains a module for setting classification thresholds, which determine at what score for the positive class the model determines that an example should be classified as belonging to the @@ -856,7 +863,7 @@

    Binary Classification Thresholds

    -

    Partial Dependence Plots#

    +

    Partial Dependence Plots

    For classification or regression models with CategoryLabel or Scalar input features, the Partial Dependence Plots module shows plots indicating the effect that changing those individual features has on model output.

    @@ -874,9 +881,10 @@

    Partial Dependence PlotsPartial Dependence Plots Module

    +

    To try this out, see https://pair-code.github.io/lit/demos/penguins.html and navigate to the “Predictions” tab.

    -

    Dive#

    +

    Dive

    Dive is a visualization module, inspired by our prior work on Facets Dive and its use in the What-If Tool, that enables @@ -900,7 +908,7 @@

    Dive#

    -

    TCAV#

    +

    TCAV

    Many interpretability methods provide importance values per input feature (e.g, token). By contrast, TCAV shows the importance of high-level concepts (e.g., color, gender, race) for a prediction @@ -918,7 +926,7 @@

    TCAV#
    -

    Example#

    +

    Example

    1.) To use TCAV, begin by creating one or more ‘concept’ slices.

    Every dataset/model is different, but for images, as low as 15 data points are shown to be sufficient. Start by adding at least 3 data points and add more as @@ -944,7 +952,7 @@

    Example#<

    TCAV2

    -

    Statistical Significance#

    +

    Statistical Significance

    One of the pitfalls with the TCAV method is the potential generating meaningless CAVs, since any randomly chosen set of images will still produce a CAV (even if it is not meaningful).

    @@ -968,13 +976,13 @@

    Statistical Significance -

    Sorting by Cosine Similarity#

    +

    Sorting by Cosine Similarity

    The option to sort examples by cosine similarity to a CAV will be available in an upcoming release.

    -

    Counterfactual Analysis#

    +

    Counterfactual Analysis

    While aggregate metrics can give a picture of overall behavior, and salience maps can give quick insight into a model’s local behavior, many questions about model behavior are best answered in a counterfactual setting: “How does my model @@ -985,7 +993,7 @@

    Counterfactual Analysis
    -

    Manual Editing#

    +

    Manual Editing

    Examples can be edited manually in the Datapoint Editor module:

    Manual Edit in the Datapoint Editor

    The “Add and Compare” button can be used to enter comparison mode, which will @@ -999,7 +1007,7 @@

    Manual EditingPair Selection Controls

    -

    Generators#

    +

    Generators

    The Generator Module supports automatic generation of counterfactuals through a variety of plug-in components:

    Generator Module

    @@ -1122,7 +1130,6 @@

    GeneratorsSalience Clustering
  • Tabular Feature Attribution
  • Pixel-based Salience
  • -
  • Attention
  • Embedding Projector
  • Aggregate Analysis @@ -288,91 +322,31 @@

    Textual Similarity (STS-B)
    -

    Sequence-to-Sequence #

    +

    Sequence-to-Sequence

    -

    Gemma #

    -

    Code: examples/lm_salience_demo.py

    +

    Gemma

    +

    Code: +examples/prompt_debugging/server.py

      -
    • Supports Gemma 2B and 7B models using KerasNLP and TensorFlow.

    • +
    • Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch) +and Transformers (with PyTorch).

    • Interactively debug LLM prompts using sequence salience.

    • Multiple salience methods (grad-l2 and grad-dot-input), at multiple -granularities: token-, word-, sentence-, and paragraph-level.

    • +granularities: token-, word-, line-, sentence-, and paragraph-level.

    Tip: check out the in-depth walkthrough at https://ai.google.dev/responsible/model_behavior, part of the Responsible Generative AI Toolkit.

    -
    -

    T5 #

    -

    Hosted instance: https://pair-code.github.io/lit/demos/t5.html
    -Code: examples/t5_demo.py

    -
      -
    • Supports HuggingFace TF2 (Keras) models as well as TensorFlow SavedModel -formats.

    • -
    • Visualize beam candidates and highlight diffs against references.

    • -
    • Visualize per-token decoder hypotheses to see where the model veers away -from desired output.

    • -
    • Filter examples by ROUGE score against reference.

    • -
    • Embeddings from last layer of model, visualized with UMAP or PCA.

    • -
    • Task wrappers to handle pre- and post-processing for summarization and -machine translation tasks.

    • -
    • Pre-loaded eval sets for CNNDM and WMT.

    • -
    -

    Tip: check out a case study for this demo on the public LIT website: -https://pair-code.github.io/lit/tutorials/generation

    -
    -
    -
    -
    -

    Language Modeling #

    -
    -

    BERT and GPT-2 #

    -

    Hosted instance: https://pair-code.github.io/lit/demos/lm.html
    -Code: examples/lm_demo.py

    -
      -
    • Compare multiple BERT and GPT-2 models side-by-side on a variety of -plain-text corpora.

    • -
    • LM visualization supports different modes:

      -
        -
      • BERT masked language model: click-to-mask, and query model at that -position.

      • -
      • GPT-2 shows left-to-right hypotheses for each target token.

      • -
      -
    • -
    • Embedding projector to show latent space of the model.

    • -
    -
    -
    -
    -
    -

    Structured Prediction #

    -
    -

    Gender Bias in Coreference #

    -

    Hosted instance: https://pair-code.github.io/lit/demos/coref.html
    -Code: examples/coref/coref_demo.py

    -
      -
    • Gold-mention coreference model, trained on -OntoNotes.

    • -
    • Evaluate on the Winogender schemas -(Rudinger et al. 2018) which test for -gendered associations with profession names.

    • -
    • Visualizations of coreference edges, as well as binary classification -between two candidate referents.

    • -
    • Stratified metrics for quantifying model bias as a function of pronoun -gender or Bureau of Labor Statistics profession data.

    • -
    -

    Tip: check out a case study for this demo on the public LIT website: -https://pair-code.github.io/lit/tutorials/coref

    -

    -

    Multimodal #

    +

    Multimodal

    -

    Tabular Data: Penguin Classification #

    +

    Tabular Data: Penguin Classification

    Hosted instance: https://pair-code.github.io/lit/demos/penguins.html
    -Code: examples/penguin_demo.py

    +Code: examples/penguin/demo.py

  • @@ -456,7 +419,6 @@

    Image Classification with MobileNet
  • Regression / Scoring @@ -493,8 +445,8 @@

    Image Classification with MobileNet + Running LIT in a Docker container - 🔥LIT 1.0 documentation - - + + @@ -69,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -113,6 +154,8 @@
    Hide table of contents sidebar
    +Skip to content +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    @@ -200,34 +250,33 @@
    -
    +
    -

    Running LIT in a Docker container#

    - +

    Running LIT in a Docker container

    +

    Users might want to deploy LIT onto servers for public-facing, long-running instances. This is how we host the LIT demos found on https://pair-code.github.io/lit/demos/. This doc describes the basic usage of LIT’s built-in demos, how to integrate your custom demo into this

    -

    Basic Usage#

    +

    Basic Usage

    LIT can be run as a containerized app using Docker or your preferred engine. This is how we run our hosted demos.

    -

    We provide a basic -Dockerfile that you can -use to build and run any of the demos in the lit_nlp/examples directory. The -Dockerfile installs all necessary dependencies for LIT and builds the +

    We provide a basic Dockerfile https://github.com/PAIR-code/lit/blob/main/Dockerfile that you can use to build and run any of the demos in the lit_nlp/examples directory. +The Dockerfile installs all necessary dependencies for LIT and builds the front-end code from source. Then it runs gunicorn as the HTTP server, invoking the get_wsgi_app() method from our demo file to get the WSGI app to serve. The options provided to gunicorn for our use-case can be found in gunicorn_config.py. You can find a reference implementation in -glue_demo.py or -lm_demo.py.

    -

    Use the following shell commands to build the default Docker image for LIT from -the provided Dockerfile, and then run a container from that image. Comments -are provided in-line to help explain what each step does.

    +glue/demo.py.

    +

    Use the following shell +https://github.com/PAIR-code/lit/blob/main/.github/workflows/ci.yml commands to build the +default Docker image for LIT from the provided Dockerfile, and then run a +container from that image. Comments are provided in-line to help explain what +each step does.

    -

    Integrating Custom LIT Instances with the Default Docker Image#

    +

    Integrating Custom LIT Instances with the Default Docker Image

    Many LIT users create their own custom LIT server script to demo or serve, which involves creating an executable Python module with a main() method, as -described in the Python API docs.

    +described in the Python API docs.

    These custom server scripts can be easily integrated with LIT’s default image as long as your server meets two requirements:

      @@ -333,7 +379,7 @@

      Integrating Custom LIT Instances with the Default Docker Image -

      Building Your Own Image#

      +

      Building Your Own Image

      Coming soon.

    @@ -409,8 +455,8 @@

    Building Your Own Image

    - + - + \ No newline at end of file diff --git a/docs/documentation/faq.html b/docs/documentation/faq.html index 18038110..ebfd57fe 100644 --- a/docs/documentation/faq.html +++ b/docs/documentation/faq.html @@ -5,11 +5,11 @@ - + Frequently Asked Questions - 🔥LIT 1.0 documentation - - + + @@ -69,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -113,6 +154,8 @@
    Hide table of contents sidebar
    +
    Skip to content +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    @@ -200,25 +250,23 @@
    -
    +
    -

    Frequently Asked Questions#

    - +

    Frequently Asked Questions

    +

    Looking for help? Submit bugs, ask questions, suggest content, and request features on our Github issues list.

    -

    Model and Data Types#

    +

    Model and Data Types

    LIT can handle a variety of models with different input and output types, and works with any modern ML framework. For more information, see Framework & Model Support.

    In addition to text, LIT has good support for different modalities, including images and tabular data. For examples, see:

    -

    Languages#

    +

    Languages

    All strings in LIT are unicode and most components use model-provided tokenization if available, so in most cases non-English languages and non-Latin -scripts should work without any modifications. For examples, see:

    -
      -
    • XNLI demo - -cross-lingual NLI, with up to 15 languages supported via a multilingual BERT -model.

    • -
    • T5 demo - -includes WMT data for machine translation

    • -
    +scripts should work without any modifications.

    -

    Scale#

    +

    Scale

    -

    Dataset Size#

    +

    Dataset Size

    LIT can comfortably handle 10k-100k datapoints, depending on the speed of the server (for hosting the model) and your local machine (for viewing the UI). When working with large datasets, there are a couple caveats:

    @@ -269,7 +310,7 @@

    Dataset Size -

    Large Models#

    +

    Large Models

    LIT can work with large or slow models, as long as you can wrap them into the model API. If you have more than a few preloaded datapoints, however, you’ll probably want to use warm_start=1 (or pass --warm_start=1 as a flag) to @@ -288,17 +329,17 @@

    Large Models -

    Privacy and Security#

    +

    Privacy and Security

    LIT allows users to query the model, as well as to view the loaded evaluation data. The LIT UI state is ephemeral and exists only in the browser window; however, model predictions and any newly-generated examples (including as manually entered in the web UI) are stored in server memory, and if --data_dir is specified, may be cached to disk.

    LIT has the ability to create or edit datapoints in the UI and then save them to -disk. If you do not want the tool to be able to write edited datapoints to -disk, then pass the --demo_mode runtime flag to the LIT server.

    +disk. If you do not want the tool to be able to write edited datapoints to disk, +then pass the --demo_mode runtime flag to the LIT server.

    -

    I have proprietary data. Is LIT secure for my team to use?#

    +

    I have proprietary data. Is LIT secure for my team to use?

    We don’t store, collect or share datasets, models or any other information loaded into LIT. When you run a LIT server, anyone with access to the web address of the server will be able to see data from the loaded datasets and @@ -312,9 +353,9 @@

    I have proprietary data. Is LIT secure for my team to use? -

    Workflow and Integrations#

    +

    Workflow and Integrations

    -

    Sending examples from another tool#

    +

    Sending examples from another tool

    LIT can read input fields directly from the URL; they should be encoded as data_<fieldname>=<value>, and field names should match those in the (default) dataset.

    @@ -323,7 +364,7 @@

    Sending examples from another tooldata0, data1, data2, e.g. data0_<fieldname>=<value>.

    -

    Downloading or exporting data#

    +

    Downloading or exporting data

    Currently, there are three ways to export data from the LIT UI:

    • In the Data Table, you can copy or download the current view in CSV format - @@ -337,7 +378,7 @@

      Downloading or exporting data -

      Loading data from the UI#

      +

      Loading data from the UI

      There is limited support for this via the settings (⚙️) menu. Select a dataset, and enter a path to load from:

      Load data from the UI

      @@ -346,7 +387,7 @@

      Loading data from the UI -

      Using components outside the LIT UI#

      +

      Using components outside the LIT UI

      Python components such as models, datasets, and generators are designed to support standalone use. These don’t depend on the LIT serving framework, and you can treat them as any other Python class and use from Colab, regular scripts, @@ -362,17 +403,64 @@

      Using components outside the LIT UI -

      Training models with LIT#

      +

      Training models with LIT

      LIT is primarily an evaluation/inference-time tool, so we don’t provide any official training APIs. However, to facilitate code reuse you can easily add training methods to your model class. In fact, several of our demos do exactly this, using LIT’s Dataset objects to manage training data along with standard training APIs (such as Keras’ model.fit()). See -quickstart_sst_demo.py -and/or -glue_models.py +glue/models.py for examples.

    +
    +

    Debug LIT UI in Colab

    +

    The LIT instance launched from CLI typically has helpful error messages in the +UI. However, this is not the case for the LIT UI in Colab and the error message +does not report any stacktrace, which makes debugging very difficult.

    +

    LIT UI error in colab

    +

    While in +Chrome developer tools, +you will be able to debug issues solely related to the frontend, but not so for +issues related to the backend or on the HTTP request path.

    +

    Thus, to show the full stacktrace, you would need to find the HTTP request sent +from the frontend to the backend, compose the same request in colab and send it +to the server.

    +
      +
    1. When rendering the UI, display it in a separate tab to make things a bit +easier to work with, e.g. lit_widget.render(open_in_new_tab=True).

    2. +
    3. Open +Chrome developer tools, +go to “Sources” tab and find the file +client/services/api_service.ts and set a +breakpoint right after where the HTTP request is set up in the queryServer +method, e.g. after this line const res = await fetch(url, {method: 'POST', body});.

      +
        +
      • Note it is possible that the whole frontend source code is compiled into +a main.js file, and the code is not exactly the same as that in LIT +frontend source code. You might have to do a bit digging to find the +right line.

      • +
      +
    4. +
    5. Go to the UI and trigger the behavior that causes the error. Now in Chrome +developer tools you will be able to see the variables and their values in +the queryServer method. Copy the values of the url and body variables +in the method.

    6. +
    7. Go back to Colab, compose your HTTP request method. Look for the main server +address printed out from lit_widget.render(open_in_new_tab=True).

    8. +
    +

    LIT colab server address

    +

    Let’s say the server address is “https://localhost:32943/?” as shown above, the +body variable obtained earlier has value “request_body_text” and the url +variable has value “./get_preds?param1=value1”. Then your HTTP request will be +like this:

    +
    ! curl -H "Content-Type: application/json" \
    +       -d "request_body_text" \
    +       -X POST "http://localhost:32943/get_preds?param1=value1"
    +
    +
    +

    Run this in Colab and you should be able to retrieve the full stacktrace of the +error.

    +
    @@ -441,6 +529,7 @@

    Training models with LITLoading data from the UI

  • Using components outside the LIT UI
  • Training models with LIT
  • +
  • Debug LIT UI in Colab
  • @@ -455,8 +544,8 @@

    Training models with LIT - + - + \ No newline at end of file diff --git a/docs/documentation/frontend_development.html b/docs/documentation/frontend_development.html index f3b9bbea..d484b264 100644 --- a/docs/documentation/frontend_development.html +++ b/docs/documentation/frontend_development.html @@ -5,11 +5,11 @@ - + Frontend Developer Guide - 🔥LIT 1.0 documentation - - + + @@ -69,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -113,6 +154,8 @@
    Hide table of contents sidebar
    +
    Skip to content +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    @@ -200,15 +250,15 @@
    -
    +
    -

    Frontend Developer Guide#

    - +

    Frontend Developer Guide

    +

    This document aims to describe the current LIT frontend system, including conventions, best practices, and gotchas.

    -

    High Level Overview#

    +

    High Level Overview

    LIT is powered by two central pieces of tech - lit-element for components and HTML rendering, and mobx for observable-oriented @@ -223,13 +273,13 @@

    High Level Overview -

    Application Architecture#

    +

    Application Architecture

    The LIT client frontend is roughly divided into three conceptual groups - Modules (which render visualizations), Services (which manage data), and the App itself (which coordinates initialization of services and determines which modules to render).

    -

    Bootstrapping#

    +

    Bootstrapping

    The LIT app bootstrapping takes place in two steps: First, the served index.html page contains a single web component for the @@ -241,16 +291,16 @@

    BootstrappingLitModule components, a process about which we’ll go into greater detail later.

    The JS bundle entry point is -main.ts, which -first imports the loaded, the <lit-app> web component is declared, and -attaches itself to the DOM, waiting for the app to be initialized.

    +main.ts, which first +imports the loaded, the <lit-app> web component is declared, and attaches +itself to the DOM, waiting for the app to be initialized.

    The second step is kicking off app initialization. The LitApp singleton class is provided with a layout declaring which LitModule components to use, then builds the app services and kicks off app initialization and loading data.

    -

    Layout#

    +

    Layout

    A layout defines the arraignment of LitModule classes in the UI. Layouts are specified in Python as LitCanonicalLayout instances, and LIT includes three pre-configured layouts in @@ -261,7 +311,7 @@

    Layout#

    default: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and

    -
  • experimental: A three-panel layout that puts exploratory data +

  • three_panel: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -270,14 +320,14 @@

    Layout#

    You can also add custom layouts to your LIT instance by defining one or more LitCanonicalLayout instances and passing them -to the server. For an example, see CUSTOM_LAYOUTS in -lm_demo.py.

    +to the server. For an example, see +prompt_debugging/layouts.py.

    Note: The pre-configured layouts are added to every LitApp instance using dictionary updates where the Mapping passed to the LitApp constructor overrides the pre-configured layouts Mapping. Thus, you can remove or change these pre-configured layouts as you like by passing a Mapping where the values of simple, default, -and/or experimental is None (to remove) or a LitCanonicalLayout instance +and/or three_panel is None (to remove) or a LitCanonicalLayout instance (to override) as you desire.

    The actual layout of components in the LIT UI, see <lit-modules>, @@ -289,7 +339,7 @@

    Layout#modules_service.

  • -

    Initialization#

    +

    Initialization

    Finally, the LIT App initializes by building the various service classes and starting the initial load of data from the server. This process consists of:

      @@ -301,7 +351,7 @@

      Initialization -

      Modules (LitModule)#

      +

      Modules (LitModule)

      The LitModule is the base class from which all module components derive. It provides a number @@ -373,7 +423,7 @@

      Modules (LitModule) -

      Setup#

      +

      Setup

      First, a LitModule must declare a static title string (1) and template function (2). The template function determines how the modules layout renders the component template and passes in module properties, such as the name of the @@ -406,7 +456,7 @@

      Setup#< other TypeScript files in different build units.

    -

    Functionality#

    +

    Functionality

    The above module has a very simple task - When the user selects input data, it makes a request to an API service to fetch and display a pig latin translation of the data. Since we’re using mobx observables to store and compute our state, @@ -445,7 +495,7 @@

    Functionality -

    Escape Hatches#

    +

    Escape Hatches

    Finally, it’s worth noting that the declarative template-based rendering setup, while effective for handling most component render logic, is sometimes inadequate for more advanced visualizations. In particular, the template @@ -475,7 +525,7 @@

    Escape Hatches -

    Stateful Child Elements#

    +

    Stateful Child Elements

    Some modules may contain stateful child elements, where the element has some internal state that can have an effect on the module that contains it. Examples of this include any modules that contain the @@ -514,7 +564,7 @@

    Stateful Child Elements

    -

    Style Guide#

    +

    Style Guide

    @@ -262,7 +310,6 @@

    Research
  • Salience Clustering
  • Tabular Feature Attribution
  • Pixel-based Salience
  • -
  • Attention
  • Embedding Projector
  • Aggregate Analysis
  • TCAV
  • @@ -370,8 +417,10 @@

    Research

    - + - + + + \ No newline at end of file diff --git a/docs/documentation/search.html b/docs/documentation/search.html index 1e5289dc..9af65ae7 100644 --- a/docs/documentation/search.html +++ b/docs/documentation/search.html @@ -1,12 +1,15 @@ - + + - Search - 🔥LIT 1.0 documentation - - + + +Search - 🔥LIT 1.0 documentation + + @@ -66,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -81,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -110,6 +154,8 @@
    Hide table of contents sidebar
    +
    Skip to content +
    @@ -127,7 +173,8 @@
    @@ -186,7 +233,8 @@
    @@ -196,7 +244,7 @@
    -
    +
    - + - + diff --git a/docs/documentation/searchindex.js b/docs/documentation/searchindex.js index 75ece15f..a95d7a50 100644 --- a/docs/documentation/searchindex.js +++ b/docs/documentation/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "components", "demos", "docker", "faq", "frontend_development", "getting_started", "glossary", "includes/highlight_demos", "index", "ui_guide"], "filenames": ["api.md", "components.md", "demos.md", "docker.md", "faq.md", "frontend_development.md", "getting_started.md", "glossary.md", "includes/highlight_demos.md", "index.md", "ui_guide.md"], "titles": ["LIT Python API", "Components and Features", "Demos", "Running LIT in a Docker container", "Frequently Asked Questions", "Frontend Developer Guide", "Getting Started with LIT", "Glossary", "<no title>", "Learning Interpretability Tool (LIT)", "UI Guide"], "terms": {"i": [0, 1, 3, 5, 6, 7, 10], "modular": 0, "compris": [0, 10], "collect": [0, 4, 5, 10], "backend": [0, 4, 5, 7, 10], 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a/docs/documentation/ui_guide.html b/docs/documentation/ui_guide.html index 0945b12d..e58dea66 100644 --- a/docs/documentation/ui_guide.html +++ b/docs/documentation/ui_guide.html @@ -5,11 +5,11 @@ - + UI Guide - 🔥LIT 1.0 documentation - - + + @@ -69,7 +69,7 @@ Light mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="feather-sun"> @@ -84,22 +84,63 @@ Dark mode + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon"> - - Auto light/dark mode + + Auto light/dark, in light mode - - - - - - - + stroke-width="1" stroke-linecap="round" stroke-linejoin="round" + class="icon-custom-derived-from-feather-sun-and-tabler-moon"> + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -113,6 +154,8 @@
    Hide table of contents sidebar
    +Skip to content +
    @@ -130,7 +173,8 @@
    @@ -186,11 +230,17 @@ Back to top
    - +
    @@ -200,16 +250,16 @@
    -
    +
    -

    UI Guide#

    - +

    UI Guide

    +

    This is a user guide for the Learning Interpretability Tool (LIT) UI.

    For a quick video tour of LIT, check out this video.

    -

    General Layout#

    +

    General Layout

    LIT lives inside a single page web application, comprised of multiple toolbars and a main section consisting of individual modules. Modules will automatically display if they are applicable to the current model and dataset; for example, @@ -227,7 +277,7 @@

    General Layout -

    Layout Options#

    +

    Layout Options

    @@ -238,7 +288,7 @@

    Layout Optionsdefault: The original LIT layout with a single group of modules on top for exploring and selecting data, and a collection of tabs supporting different analytical tasks on the bottom; and

    -
  • experimental: A three-panel layout that puts exploratory data +

  • three_panel: A three-panel layout that puts exploratory data visualizations at full-page height on the left, tools for inspecting and manipulating examples and their associated predictions in the upper right, and a collection of tabs supporting different analytical tasks in the lower @@ -248,7 +298,7 @@

    Layout Options -

    Datapoint Selections#

    +

    Datapoint Selections

    LIT displays a loaded dataset and its model results across the set of selected models. Users can dive into detailed results by selecting datapoints from the dataset.

    @@ -278,16 +328,16 @@

    Datapoint SelectionsMetrics Module section.

  • -

    Toolbars#

    +

    Toolbars

    There are three toolbars provided in LIT. The top bar includes the selected model(s) and dataset, a settings button, and URL sharing functionality. Below that is the main toolbar with the menus and controls for navigation and selection. At the bottom of the page is a status bar.

    LIT toolbars

    -

    Top Bar#

    +

    Top Bar

    -

    Global Settings#

    +

    Global Settings

    The global settings dialog is accessible through the “Configure” button in the top bar.

    LIT can be launched with a set of models and datasets. The settings screen @@ -302,7 +352,7 @@

    Global Settings

    -

    URL Sharing#

    +

    URL Sharing

    Much of the LIT app’s state — the loaded models and datasets, selected datapoints, minimized and/or full-screen modules — is stored in URL parameters. The “Copy Link” button in the top bar allows a user to share @@ -313,25 +363,35 @@

    URL Sharing -

    Main Toolbar#

    +

    Main Toolbar

    The main toolbar is right below the top bar and contains a number of different controls and information. The left side of the toolbar contains a set of menus for quickly controlling datapoint selection and coloring. This includes the following controls:

      -
    • The “Select related” option adds any datapoints “related” to the current -selection. In LIT, “related” is defined as datapoints created from some -source datapoint (through manual editing or a datapoint generator), or a -source datapoint that a selected datapoint was created from.

    • -
    • The “Clear selection” button deselects all selected datapoints. The -dropdown also contains a color legend for the current color setting.

    • -
    • The Slices option allows quick selection of an already-created slice of -datapoints.

    • -
    • The “Datapoint color” menu enables setting of the color of each -datapoint in the modules that visualize all datapoints (such as the -Embeddings and Scalars modules) by any number of datapoint features or -model outputs on those datapoints (such as coloring by some categorical -input feature, or by prediction error for a regression task).

    • +
    • The “Select datapoint” menu provides a drop-down of several options:

      +
        +
      • the “Random” option selects a random datapoint,

      • +
      • the “All related” option adds any datapoints “related” to the +current selection. In LIT, “related” is defined as datapoints created +from some source datapoint (through manual editing or a datapoint +generator), or a source datapoint that a selected datapoint was created +from,

      • +
      • the “Parents” option adds the source datapoints that the selected +datapoints were created from,

      • +
      • the “Children” option adds the datapoints created from the selected +datapoints (through manual editing or a datapoint generator),

      • +
      • the Slices option allows quick selection of an already-created slice +of datapoints,

      • +
      • the “Clear selection” button deselects all selected datapoints.

      • +
      +
    • +
    • The “Color by” menu enables setting of the color of each datapoint in +the modules that visualize all datapoints (such as the Embeddings and +Scalars modules) by any number of datapoint features or model outputs on +those datapoints (such as coloring by some categorical input feature, or by +prediction error for a regression task).

    • +
    • The Slices menu allows adding/selecting/removing slices of datapoints.

    Next to the menus is a button for pinning/unpinning a datapoint. Pinning a datapoint puts LIT into datapoint comparison mode, where two datapoints can be @@ -349,7 +409,7 @@

    Main Toolbar -

    Status Bar#

    +

    Status Bar

    The status bar at the bottom of the tool contains a text area on the left side. If the tool is currently waiting on the results of a call to the backend (such as for running predictions or getting embeddings), this information will be @@ -362,7 +422,7 @@

    Status Bar -

    Comparing Models#

    +

    Comparing Models

    By loading more than one model in the global settings controls, LIT can compare multiple models. A subset of modules that show per-model information are then duplicated to allow easy comparison across two models. Other modules, such the @@ -371,7 +431,7 @@

    Comparing Models

    -

    Comparing Datapoints#

    +

    Comparing Datapoints

    Pinning a datapoint, through either the toolbar button or controls in modules (e.g., the pin icons in Data Table rows), puts LIT into datapoint comparison mode. In this mode, the pinned datapoint is used as a reference to compare the @@ -386,7 +446,7 @@

    Comparing DatapointsLIT datapoint comparison

    -

    Slices#

    +

    Slices

    The Slice Editor allow users to create, edit, select, and delete slices. The current selection can be saved as a slice by giving it a name and clicking “Create slice”. The slice list allows you to select any of the previously-saved @@ -405,14 +465,14 @@

    Slices#LIT slice controls

    -

    Module Details#

    +

    Module Details

    This section contains details on using and interacting with individual modules that are built into LIT. Note that this list may not be complete and additional modules can be created and used in LIT by clients.

    All modules can be toggled to be shown full-screen through use of the full-screen button in the top-right of each module.

    -

    Embedding Projector#

    +

    Embedding Projector

    When using LIT with a model that returns embeddings (or activations) in addition to predictions, the embedding projector will show all datapoints by their embeddings projected down to 3 dimensions. This is useful for exploring and @@ -430,7 +490,7 @@

    Embedding Projector -

    Data Table#

    +

    Data Table

    The data table shows all datapoints in a simple table. Datapoints can be selected or unselected through a click. Shift+click allows selecting a set of consecutive datapoints, and control+click allows selecting a set of individual @@ -483,12 +543,12 @@

    Data Table -

    Datapoint Editor#

    +

    Datapoint Editor

    The datapoint editor shows the details of the primary selected datapoint, if one is selected. Any field can be edited, and a new datapoint created with those -edits through the “Make new datapoint” button. Any edit to an existing -datapoint must be saved as a new datapoint to be explored, to keep datapoints -immutable for simplicity of use.

    +edits through the “Add” button. Any edit to an existing datapoint must be +saved as a new datapoint to be explored, to keep datapoints immutable for +simplicity of use.

    When no datapoint is selected, the editor shows a blank datapoint that can be filled out by hand to create a completely new datapoint.

    Features shown with a “(*)” next to their name are required as model input and @@ -496,7 +556,7 @@

    Datapoint EditorLIT datapoint editor

    -

    Datapoint Generator#

    +

    Datapoint Generator

    The datapoint generator module allows creation of new datapoints from all currently-selected datapoints (or the entire dataset if no datapoints are selected) through a set of counterfactual datapoint generators. These generators @@ -531,7 +591,7 @@

    Datapoint Generator

    -

    Metrics Table#

    +

    Metrics Table

    The metrics table shows model metrics for each model in a table format. The exact metric types are determined by the python metrics component that calculates metrics given the model types being evaluated. These can include @@ -553,7 +613,7 @@

    Metrics Table

    -

    Confusion Matrix#

    +

    Confusion Matrix

    The confusion matrix buckets all datapoints from the dataset (or the current selection, if one is made) into buckets in a 2D matrix. This is normally used to compare classification predictions on a model versus the ground truth classes of @@ -571,7 +631,7 @@

    Confusion Matrix

    -

    Scalars#

    +

    Scalars

    The scalars module shows a set of scatter or jitter plots, one for each scalar output of a loaded model (such as a regression score, or a classification score for a specific class). Each of them contains all datapoints in the dataset, laid @@ -595,7 +655,7 @@

    Scalars#<

    LIT prediction scores

    -

    Model Output#

    +

    Model Output

    Model output modules show the result of a model on the primary selected datapoint. The visuals of these modules depend on the model task being performed. For a simple classification task, it will show the class scores from @@ -608,7 +668,7 @@

    Model OutputLIT structured prediction

    -

    Salience Maps#

    +

    Salience Maps

    Salience maps show the influence of different parts of inputs features on a model’s prediction on the primary selection. This module can contain multiple methodologies for calculating this salience, depending on the capabilities of @@ -618,28 +678,20 @@

    Salience Maps

    -
    -

    Attention#

    -

    For models that return attention head information along with predictions, the -attention module shows which tokens are attended to between layers of a model. -Dropdowns allow you to select which layer and attention head is being -visualized. Line opacity is controlled by the magnitude of the attention between -those tokens.

    -

    LIT attention

    -
    -

    User Journeys#

    +

    User Journeys

    In this section, we explore some example user journeys and how LIT enables them.

    -

    Sentiment Analysis#

    +

    Sentiment Analysis

    How well does a sentiment classifier handle negation? We load the development set of the Stanford Sentiment Treebank, and use the search function in LIT’s data table to find the 56 datapoints containing the word “not”. Looking at the @@ -656,48 +708,13 @@

    Sentiment Analysis

    -
    -

    Gender Bias in Coreference#

    -

    Does a system encode gendered associations, which might lead to incorrect -predictions? We load a coreference model trained on OntoNotes, and load the -Winogender dataset into LIT for evaluation. Each Winogender example has a -pronoun and two candidate referents, one a occupation term like (“technician”) -and one is an “other participant” (like “customer”). Our model predicts -coreference probabilities for each candidate. We can explore the model’s -sensitivity to pronouns by comparing two examples side-by-side (see structured -predictions in screenshot). We can see how commonly the model makes similar -errors by paging through the dataset, or by selecting specific slices of -interest.

    -

    Winogender analysis

    -

    In the Metrics table, we can slice this selection by pronoun type and by the -true referent. On the set of male-dominated occupations (< 25% female by BLS), -we see the model performs well when the ground-truth agrees with the -stereotype - e.g. when the answer is the occupation term, male pronouns are -correctly resolved 83% of the time, compared to female pronouns only 37.5% of -the time (screenshot section (c)).

    -

    Winogender analysis

    -
    -
    -

    Debugging Text Generation#

    - -

    Does the training data explain a particular error in text generation? We analyze -an older T5 model on the CNN-DM summarization task. LIT’s Scalars module -allows us to look at per-example ROUGE scores, and quickly select an example -with middling performance (screenshot section (a)). We find the generated text -(screenshot section (b)) contains an erroneous constituent: “alastair cook was -replaced as captain by former captain …”. We can dig deeper, using LIT’s -language modeling module (screenshot section (c)) to see that the token “by” is -predicted with high probability (28.7%).

    -

    To find out how T5 arrived at this prediction, we utilize the “similarity -searcher” component through the datapoint generator (screenshot section (d)). -This performs a fast approximate nearest-neighbor lookup from a pre-built index -over the training corpus, using embeddings from the T5 decoder. With one click, -we can retrieve 25 nearest neighbors and add them to the LIT UI for inspection. -We see that the words “captain” and “former” appear 34 and 16 times in these -examples–along with 3 occurrences of “replaced by” (screenshot section (e)), -suggesting a strong prior toward our erroneous phrase.

    -

    LIT sequence-to-sequence analysis

    +
    +

    Sequence salience

    +

    Sequence salience generalizes token-based salience to text-to-text models, +allowing you to explain the impact of the prompt tokens on parts of the model +output.

    +

    Check out here for more details on how to +navigate the Sequence Salience UI module.

    @@ -786,13 +803,11 @@

    Debugging Text GenerationScalars
  • Model Output
  • Salience Maps
  • -
  • Attention
  • User Journeys
  • @@ -807,8 +822,8 @@

    Debugging Text Generation - + - + \ No newline at end of file diff --git a/docs/index.html b/docs/index.html index d6b44ac4..65c7ceaa 100644 --- a/docs/index.html +++ b/docs/index.html @@ -110,7 +110,6 @@

    Built-in capabilities

    Salience maps

    -

    Attention visualization

    Metrics calculations

    Counterfactual generation

    Model and datapoint comparison

    @@ -151,7 +150,7 @@

    What's the latest

    diff --git a/docs/tutorials/coref/index.html b/docs/tutorials/coref/index.html deleted file mode 100644 index ea57ac9d..00000000 --- a/docs/tutorials/coref/index.html +++ /dev/null @@ -1,187 +0,0 @@ - - - - - - - - - - - Gender Bias in Coreference - - - - - - - - - - - - - - - -
    - -
    - -
    - - - -
    -
    -
    -
    - Tutorials > Analysis > Coreference -
    -

    Gender Bias in Coreference

    - -

    Or, run your own with examples/coref/coref_demo.py

    -

    Does a system encode gendered associations, which might lead to incorrect predictions? We explore this for coreference, the task of identifying whether two mentions refer to the same (real-world) entity. For example, in the sentence "The technician told the customer that they could pay with cash.", we understand from the context that "they" refers to "the customer", the one paying.

    -

    The Winogender dataset introduced by Rudinger et al. 2018 presents a set of challenging coreference examples designed to explore gender bias. It consists of 120 templates, each with semantic context that makes it easy for humans to tell the answer. Each template is instantiated with different pronouns, in order to give a minimal pair:

    -
      -
    • "The technician told the customer that he could pay with cash."
    • -
    • "The technician told the customer that she could pay with cash."
    • -
    -

    In both cases, the pronoun should refer to the customer - but does our model agree? Or does it fall back on stereotypes about who can be a technician, or a customer? We can use LIT to explore this interactively, making use of the side-by-side functionality, structured prediction visualization, and powerful features for aggregate analysis to validate our findings.

    -

    We load our coreference model into LIT, along with a copy of the Winogender dataset. Our model predicts probabilities for each mention pair - in this case the (occupation, pronoun) and (participant, pronoun) pairs - and LIT renders this as a pair of edges:

    -
    - -
    - -
    -
    Above: A coreference prediction.
    -
    -

    We can select an example by clicking the row in the data table in the top left of the UI; the predictions will display automatically in the "Predictions" tab below. To look at two predictions side-by-side, we can enable "Compare datapoints" mode in the toolbar, which will pin our first selection as a "reference" and allow us to select another point to compare:

    -
    - -
    - -
    -
    Above: Selecting two datapoints to compare.
    -
    -

    We see that LIT automatically replicates the predictions view, allowing us to see how our model handles "he" and "she" differently on these two sentences:

    -
    - -
    - -
    -
    Above: Comparing coreference predictions of two datapoints.
    -
    -

    To see why this might be, we can make use of some additional information from the U.S. Bureau of Labor Statistics (BLS), which tabulates the gender percentages in different occupations. Our example loads this along with the dataset, and LIT shows this as a column in the data table:

    -
    - -
    - -
    -
    Above: Datapoints with extra informational feature columns.
    -
    -

    We see that "technician" is only 40% female, suggesting that our model might be picking up on social biases with its eagerness to identify "he" as the technician in the example above.

    -

    Is this a pattern? In addition to individual instances, we can use LIT to see if this holds on larger slices of the data. Turning to the "Performance" tab, we see that our model gets around 63% accuracy overall.

    -
    - -
    - -
    -
    Above: Overall model accuracy in the metrics table.
    -
    -

    Let's see how this breaks down. On the right, the Scalars module lets us select data based on scalar values, such as the percent female of each profession according to BLS. Let's select the points on the left, with professions that are stereotypically male (< 25% female). Additionally, we'll stratify our metrics based on the pronoun group, and whether the answer should be the occupation term or the other, neutral, participant:

    -
    - -
    - -
    -
    Above: Metrics faceted into sub-groups and scalar results plots.
    -
    -

    We can see that on this slice, our model performs very well when the ground truth agrees with the stereotype - i.e. when the answer is the occupation term, our model resolves male pronouns correctly 91% of the time, while only matching female pronouns 37% of the time in exactly the same contexts.

    - -
    -
    -
    -
    time to read
    -
    10 minutes
    -
    takeaways
    -
    Learn about how to explore fairness using datapoint comparison and metrics comparisons.
    -
    -
    - -
    - - - -
    - -
    - - - - - - - diff --git a/docs/tutorials/generation/index.html b/docs/tutorials/generation/index.html deleted file mode 100644 index 6647a8b1..00000000 --- a/docs/tutorials/generation/index.html +++ /dev/null @@ -1,149 +0,0 @@ - - - - - - - - - - - Debugging a Text Generator - - - - - - - - - - - - - - - -
    - -
    - -
    - - - -
    -
    -
    -
    - Tutorials > Analysis > Generator -
    -

    Debugging a Text Generator

    - -

    Or, run your own with examples/t5_demo.py

    -

    Does the training data explain a particular error in text generation? We can use LIT to analyze a T5 model on the CNN-DM summarization task. LIT’s scalar plot module allows us to look at per-example ROUGE scores, and quickly select an example with middling performance.

    -
    - -
    - -
    -
    Above: The datapoint of interest, with its inaccurate summarization.
    -
    -

    We find the generated text contains an erroneous subject: "[A]lastair [C]ook was replaced as captain by former captain ...". In reading the input text, we can see that he was replaced as captain by Eoin Morgan, and not by a former captain. Another former captain does have a quote in the input text, and the model seemed to confuse these two subjects when generating its summary.

    -
    - -
    - -
    -
    Above: Finding the most similar datapoints from the training set.
    -
    -

    To investigate how T5 arrived at this prediction, we utilize the “similarity searcher” component through the counterfactual generator tab. This performs a fast approximate nearest-neighbor lookup from a pre-built index over the training corpus, using embeddings -from the T5 decoder. With one click, we retrieve the 25 nearest neighbors to our datapoint of interest from the training set and add them to the LIT UI for inspection. We can see through the search capability in the data table that the words “captain” and “former” appear 34 and 16 times in these examples–along with 3 occurrences of “replaced by” and two occurrences of “by former”. This suggests a strong prior toward our erroneous phrase from the training data most related to our datapoint of interest.

    -
    - -
    - -
    -
    Above: An example of the existence of "by former" in a similar datapoint from the training set.
    -
    - -
    -
    -
    -
    time to read
    -
    3 minutes
    -
    takeaways
    -
    Learn about how to debug a prediction on a datapoint by searching for similar datapoints from the training set.
    -
    -
    - -
    - - - -
    - -
    - - - - - - - diff --git a/docs/tutorials/index.html b/docs/tutorials/index.html index d1d43d44..c415c6ce 100644 --- a/docs/tutorials/index.html +++ b/docs/tutorials/index.html @@ -115,14 +115,6 @@
    - -

    diff --git a/docs/tutorials/sentiment/index.html b/docs/tutorials/sentiment/index.html index 017f1694..1ee96c27 100644 --- a/docs/tutorials/sentiment/index.html +++ b/docs/tutorials/sentiment/index.html @@ -77,7 +77,7 @@

    Exploring a Sentiment Classifier

    -

    Or, run your own with examples/glue_demo.py

    +

    Or, run your own with examples/glue/demo.py

    How well does a sentiment classifier handle negation? We can use LIT to interactively ask this question and get answers. We loaded up LIT the development set of the Stanford Sentiment Treebank (SST), which contains sentences from movie reviews that have been human-labeled as having a negative sentiment (0), or a positive sentiment (1). For a model, we are using a BERT-based binary classifier that has been trained to classify sentiment.

    Using the search function in LIT’s data table, we find the 67 datapoints containing the word “not”. By selecting these datapoints and looking at the Metrics Table, we find that our BERT model gets 91% of these correct, which is slightly higher than the accuracy across the entire dataset.

    diff --git a/docs/tutorials/sequence-salience/index.html b/docs/tutorials/sequence-salience/index.html index 9e76d614..e499ef9a 100644 --- a/docs/tutorials/sequence-salience/index.html +++ b/docs/tutorials/sequence-salience/index.html @@ -14,7 +14,7 @@ gtag('config', 'G-Q74F5RJLXB'); - Prompt Engineering with Sequence Salience + Prompt Debugging with Sequence Salience @@ -73,11 +73,11 @@
    - Tutorials > Analysis > Prompt Engineering with Sequence Salience + Tutorials > Analysis > Prompt Debugging with Sequence Salience
    -

    Prompt Engineering with Sequence Salience

    +

    Prompt Debugging with Sequence Salience

    -

    Or, run this locally with examples/lm_salience_demo.py

    +

    Or, run this locally with examples/prompt_debugging/server.py

    Large language models (LLMs), such as Gemini and GPT-4, have become ubiquitous. Recent releases of "open weights" models, including Llama 2, Mistral, and Gemma, have made it easier @@ -294,8 +294,7 @@

    Case Study 2: Assessing Constitutional Principles in Prompts

    them in prompt engineering use cases.

    Case Study 3: Side-by-Side Behavior Comparisons

    LIT support a side-by-side (SxS) mode that can be used to compare two -models, or here, compare model behavior on two related examples. (Check out the -gender bias tutorial for another case study in SxS analysis.) +models, or here, compare model behavior on two related examples. Let's see how we can use this to understand differences in prompt designs with Sequence Salience.

    GSM8K is a benchmark dataset of grade school math problems commonly @@ -441,8 +440,8 @@

    Conclusion

    research area. The LIT team has provided reference implementations for computing gradient-based salience— Grad L2 Norm and Grad · Input—for LLMs in two -popular frameworks: KerasNLP and -HuggingFace Transformers.

    +popular frameworks: KerasNLP and +HuggingFace Transformers.

    There is considerable opportunity to research how the model analysis foundations described in this tutorial can support richer workflows, particularly as they relate to aggregate analysis of salience results over many examples, and the @@ -469,7 +468,7 @@

    Further Reading

    time to read
    20 minutes
    takeaways
    -
    Learn to use LIT's Sequence Salience module for prompt engineering.
    +
    Learn to use LIT's Sequence Salience module for prompt debugging.

    Salience Maps for Text

    -

    Or, run your own with examples/glue_demo.py

    +

    Or, run your own with examples/glue/demo.py

    LIT enables users to analyze individual predictions for text input using salience maps, for which gradient-based and/or blackbox methods are available. In this tutorial, we will explore how to use salience maps to analyze a text diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 00000000..faafe41b --- /dev/null +++ b/package-lock.json @@ -0,0 +1,6 @@ +{ + "name": "lit", + "lockfileVersion": 2, + "requires": true, + "packages": {} +} diff --git a/website/package-lock.json b/website/package-lock.json deleted file mode 100644 index c1aca5cc..00000000 --- a/website/package-lock.json +++ /dev/null @@ -1,8662 +0,0 @@ -{ - "name": "lit-website", - "version": "1.0.0", - "lockfileVersion": 2, - "requires": true, - "packages": { - "": { - "name": "lit-website", - "version": "1.0.0", - "license": "Apache-2.0", - "devDependencies": { - "@11ty/eleventy-plugin-syntaxhighlight": "^3.0.4", - "markdown-it": "^11.0.0" - } - }, - "node_modules/@11ty/dependency-tree": { - "version": "2.0.1", - "resolved": "https://registry.npmjs.org/@11ty/dependency-tree/-/dependency-tree-2.0.1.tgz", - "integrity": "sha512-5R+DsT9LJ9tXiSQ4y+KLFppCkQyXhzAm1AIuBWE/sbU0hSXY5pkhoqQYEcPJQFg/nglL+wD55iv2j+7O96UAvg==", - 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