You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
[ ] Sentencevectors:
Global:
[ ] Remove normalized vector files and replace with NN
ANN: --> (Annoy, with Option for Google ScANN?)
[ ] Only construct index when when calling most_similar method
[ ] Logging of index speed
[ ] Save and load of index
[ ] Assert that index and vectors are of equal size
[ ] Paramters must be tunable afterwards
[ ] Method to reconstruct index
[ ] How does the index saving comply with SaveLoad?
[ ] Write unittests?
Brute:
[ ] Keep access to default method
[ ] Make ANN Search the default?! --> Results?
[ ] Throw warning for large datasets for vector norm init
[ ] Maybe throw warning if exceeds RAM size of the embedding + normalization
Other:
[ ] L2 Distance
[ ] L1 Distance
[ ] Correlation (Power Score Correlation?)
[ ] Lookup-Functionality (via defaultdict)
[ ] Get vector: Not really memory friendly
[ ] Show which words are in vocabulary
[ ] Asses empty vectors (via EPS sum)
[ ] Z-Score Transformation from Power-Means Embedding? --> Benefit?
The text was updated successfully, but these errors were encountered:
[ ] Sentencevectors:
Global:
[ ] Remove normalized vector files and replace with NN
ANN: --> (Annoy, with Option for Google ScANN?)
[ ] Only construct index when when calling most_similar method
[ ] Logging of index speed
[ ] Save and load of index
[ ] Assert that index and vectors are of equal size
[ ] Paramters must be tunable afterwards
[ ] Method to reconstruct index
[ ] How does the index saving comply with SaveLoad?
[ ] Write unittests?
Brute:
[ ] Keep access to default method
[ ] Make ANN Search the default?! --> Results?
[ ] Throw warning for large datasets for vector norm init
[ ] Maybe throw warning if exceeds RAM size of the embedding + normalization
Other:
[ ] L2 Distance
[ ] L1 Distance
[ ] Correlation (Power Score Correlation?)
[ ] Lookup-Functionality (via defaultdict)
[ ] Get vector: Not really memory friendly
[ ] Show which words are in vocabulary
[ ] Asses empty vectors (via EPS sum)
[ ] Z-Score Transformation from Power-Means Embedding? --> Benefit?
The text was updated successfully, but these errors were encountered: