simple_transformer_french_to_fongbe_and_ewe.ipynb

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   "source": "![Picture title](image-20210618-125713.png)",
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   "source": "# Machine translation using `simpletransformers` *French to Fongbe and Ewe*\n\n[![simple](https://img.shields.io/badge/Simple_Transformers-v0.61.6-0c0c0c?logo=FutureLearn&logoColor=white&style=for-the-badge)](https://github.com/ThilinaRajapakse/simpletransformers)\n\nThis library is based on the Transformers library by HuggingFace. Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize a model, train the model, and evaluate a model.\n\n**Supports**\n\n- Sequence Classification\n- Token Classification (NER)\n- Question Answering\n- Language Model Fine-Tuning\n- Language Model Training\n- Language Generation\n- T5 Model\n- Seq2Seq Tasks\n- Multi-Modal Classification\n- Conversational AI.\n- Text Representation Generation.",
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  {
   "cell_type": "markdown",
   "source": "[![Picture title](image-20210601-095719.png)](https://zindi.africa/competitions/ai4d-takwimu-lab-machine-translation-challenge/leaderboard)",
   "metadata": {
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  {
   "cell_type": "markdown",
   "source": "The objective of this challenge is to create a machine translation system capable of converting text from French into Fongbe or Ewe. You may train one model per language or create a single model for both. You may not use any external data, so a key component of this competition is finding a way to work with the available data efficiently.",
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   "cell_type": "markdown",
   "source": "# installing simple transformers",
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   "source": "!pip install simpletransformers\n!pip install fsspec==2021.5.0",
   "execution_count": null,
   "outputs": []
  },
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   "source": "from google.colab import drive\ndrive.mount('/content/drive')",
   "execution_count": null,
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    {
     "output_type": "stream",
     "text": "Mounted at /content/drive\n",
     "name": "stdout"
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  {
   "cell_type": "markdown",
   "source": "# Data\nI have used only 35k samples so that my GPU dosent run out of memory and I have used original data without preprocessing.",
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   "source": "import pandas as pd\nfrom sklearn.model_selection import train_test_split\n\ndf = pd.read_csv(\"/content/drive/MyDrive/Fon_and_ewe/Train.csv\")[0:35000]# Change Train Data directory\ntest = pd.read_csv(\"/content/drive/MyDrive/Fon_and_ewe/Test.csv\")# Change Test Data Directory",
   "execution_count": null,
   "outputs": []
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   "source": "Clean = False\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "Cleaning function was use full in english translation but in this case the we get poor results if we clean the data.",
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   "source": "if Clean:    \n    # converting every letter to lower case\n    df['Target'] = df['Target'].apply(lambda x: str(x).lower())\n    df['French'] = df['French'].apply(lambda x: str(x).lower())\n    # removing apostrophe from the sentences\n    df['Target'] = df['Target'].apply(lambda x: re.sub(\"'\",\"\",x))\n    df['French'] = df['French'].apply(lambda x: re.sub(\"'\",\"\",x))\n    exclude = set(string.punctuation)\n    # removing all the punctuations\n    df['Target'] = df['Target'].apply(lambda x: ''.join(ch for ch in x if ch not in exclude))\n    df['French'] = df['French'].apply(lambda x: ''.join(ch for ch in x if ch not in exclude))\n    # removing digits from the sentences\n    digit = str.maketrans('','',string.digits)\n    df['Target'] = df['Target'].apply(lambda x: x.translate(digit))\n    df['French'] = df['French'].apply(lambda x: x.translate(digit))",
   "execution_count": null,
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  {
   "cell_type": "markdown",
   "source": "Dividing train and test data into two dataframe based on languagues.",
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   "source": "Fon = df[df.Target_Language==\"Fon\"]\nEwe = df[df.Target_Language==\"Ewe\"]\nFon_test = test[test.Target_Language==\"Fon\"]\nEwe_test = test[test.Target_Language==\"Ewe\"]",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "# Training `Fon` Model\nUsing simple transformer seq2seq I have downloaded `Helsinki-NLP/opus-mt-en-mul` which work best in our case and using specific `Seq2SeqArgs` to set arguments of model.\n\n**Args**\n- num_train_epochs = 30\n- batch_size = 32\n- max_length = 120\n- src_lang =\"fr\"\n- tgt_lang =\"fon\"\n- overwrite_output_dir = True\n",
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   "source": "import logging\n\nimport pandas as pd\nfrom simpletransformers.seq2seq import (\n    Seq2SeqModel,\n    Seq2SeqArgs,\n)\n\n\nlogging.basicConfig(level=logging.INFO)\ntransformers_logger = logging.getLogger(\"transformers\")\ntransformers_logger.setLevel(logging.WARNING)\n\ntrain_data = Fon[[\"French\",\"Target\"]]\n\ntrain_data = train_data.rename(columns={\"French\":\"input_text\",\"Target\":\"target_text\"})\ntrain_df, eval_df = train_test_split(train_data, test_size=0.2, random_state=42)\n\n\nmodel_args = Seq2SeqArgs()\nmodel_args.num_train_epochs = 30\nmodel_args.no_save = True\nmodel_args.evaluate_generated_text = False\nmodel_args.evaluate_during_training = False\nmodel_args.evaluate_during_training_verbose = True\nmodel_args.rag_embed_batch_size = 32\nmodel_args.max_length = 120\nmodel_args.src_lang =\"fr\"\nmodel_args.tgt_lang =\"fon\"\nmodel_args.overwrite_output_dir = True\n\n# Initialize model\nmodel_fon = Seq2SeqModel(\n    encoder_decoder_type=\"marian\",\n    encoder_decoder_name=\"Helsinki-NLP/opus-mt-en-mul\",\n    args=model_args,\n    use_cuda=True,\n)\n\n\ndef count_matches(labels, preds):\n    print(labels)\n    print(preds)\n    return sum(\n        [\n            1 if label == pred else 0\n            for label, pred in zip(labels, preds)\n        ]\n    )\n\n\n\n\n\n",
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   "source": "## Training Model",
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      "a4ca9dd77f09422cb482e561c07b25d6",
      "04d9fabf0ef74e748f26b356901cfa6c"
     ]
    },
    "id": "P94d75AGsBBv",
    "outputId": "d11c312f-9ce2-4b6f-bd32-bb1e18a996cc",
    "cell_id": "00008-97cf4e1d-12cd-4a78-bb1a-85340ca5ff02",
    "deepnote_cell_type": "code"
   },
   "source": "# Train the model\nmodel_fon.train_model(\n    train_df, eval_data=eval_df, matches=count_matches\n)\n",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_utils: Creating features from dataset file at cache_dir/\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85827cb9ead3437e9b5244945c560745",
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     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_model: Training started\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2dd0f4402205442c9b5504a1abaf5d12",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Epoch:   0%|          | 0/30 [00:00<?, ?it/s]"
     },
     "metadata": {
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     }
    },
    {
     "output_type": "display_data",
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       "model_id": "156cab375bc04558915acde9f898b038",
       "version_minor": 0,
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      "text/plain": "Running Epoch 0 of 30:   0%|          | 0/2468 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-2000\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-2468-epoch-1\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
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       "model_id": "c587823df1364613a86ad45a8ac9cd58",
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     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
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     "name": "stderr"
    },
    {
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       "model_id": "cdc9870e892e458fa9fc5dacafcc1cc8",
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      "text/plain": "Running Epoch 2 of 30:   0%|          | 0/2468 [00:00<?, ?it/s]"
     },
     "metadata": {
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     }
    },
    {
     "output_type": "stream",
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     "name": "stderr"
    },
    {
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       "model_id": "7ea066da05e74fed9c985e04577492bd",
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    },
    {
     "output_type": "stream",
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     "name": "stderr"
    },
    {
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       "model_id": "fb44eb319e714d9e90c949d421af1915",
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    },
    {
     "output_type": "stream",
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     "name": "stderr"
    },
    {
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    {
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    },
    {
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    },
    {
     "output_type": "stream",
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    },
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
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    },
    {
     "output_type": "stream",
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    },
    {
     "output_type": "display_data",
     "data": {
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       "model_id": "efe235ef5ba441808cd6efad3c283694",
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     "metadata": {
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    },
    {
     "output_type": "stream",
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    },
    {
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     "data": {
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    {
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    },
    {
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    {
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    },
    {
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    {
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    },
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    {
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       "version_major": 2
      },
      "text/plain": "Running Epoch 24 of 30:   0%|          | 0/2468 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-60000\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-61700-epoch-25\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b00a67facec483a888f9de7f24b7b35",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Running Epoch 25 of 30:   0%|          | 0/2468 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-62000\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-64000\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-64168-epoch-26\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef33fd27c1364bb28970ea55d2a419a6",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Running Epoch 26 of 30:   0%|          | 0/2468 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-66000\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-66636-epoch-27\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c1ad96de45940e9a387e00d377263bd",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Running Epoch 27 of 30:   0%|          | 0/2468 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-68000\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-69104-epoch-28\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ba7cc315dd248dca96d602deda5a1d5",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Running Epoch 28 of 30:   0%|          | 0/2468 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-70000\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-71572-epoch-29\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee5a5a328c8945bbbb2f8111249fc6cb",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Running Epoch 29 of 30:   0%|          | 0/2468 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-72000\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-74000\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-74040-epoch-30\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/\nINFO:simpletransformers.seq2seq.seq2seq_model: Training of Helsinki-NLP/opus-mt-en-mul model complete. Saved to outputs/.\n",
     "name": "stderr"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(74040, 0.6869546678079066)"
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Model Evaluation",
   "metadata": {
    "tags": [],
    "cell_id": "00015-5267c348-ff97-4689-ae87-969ff06a18ce",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "trusted": true,
    "id": "6Nf1QAcgsBBw",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
      "18b670030a454b4787063e98e7ac010b",
      "301a1f88491f493c8d8bfc981af702c5",
      "49287654894e431d874cb0c1d2b2a89c",
      "730b8c79486047d7bc8341c7f9bfd929",
      "605fdc7e41e14acb8e274552347927d3",
      "3cba1808b9ff43ad9985acf1c7944630",
      "d5055b4983bf47d99419541fe563eae5",
      "333ec578b15d45c7aab861df449d824a",
      "cfaba58cb4d44e33929e00eb40159890",
      "e23a796b61444b678d58037cdd4dfd6b",
      "68a8859e60bb4854a6ee02022fed7fe2"
     ]
    },
    "outputId": "765472eb-0df0-4b0b-cba4-2df336283ab7",
    "cell_id": "00009-c262235e-d96a-4c5a-87e4-fe53dffef2e6",
    "allow_embed": "code_output",
    "output_cleared": true,
    "deepnote_cell_type": "code"
   },
   "source": "# # Evaluate the model\nresults = model_fon.eval_model(eval_df)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "trusted": true,
    "id": "0CiMDYBWsBBx",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 164
    },
    "outputId": "8017c11b-7f94-4e03-afd9-c7c8571150db",
    "cell_id": "00010-2a34717b-dd68-4650-810f-f02d632d3a52",
    "output_cleared": true,
    "deepnote_cell_type": "code"
   },
   "source": "results",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Sample Prediction",
   "metadata": {
    "tags": [],
    "cell_id": "00018-66748f6d-5def-4d82-be0b-31a5f2f77fbb",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "trusted": true,
    "id": "yjFZ4NkWsBBy",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 120,
     "referenced_widgets": [
      "32159e862aba44f59e253150fefb9aea",
      "614f39c70e994c43b7e93137f86d05df",
      "85c224881bf8458fbc068042dc759726",
      "533be814e2384a3388e95be6cb364582",
      "ed680601b7df4f5e885c2ed9c04d3ccc",
      "12bebe2951924d5a81fa1b832e9c742d",
      "c7b95d106e354609906288ce7c4574ef",
      "3734adcbe40d4f0591778af6fc7d40b4",
      "70b1bd43884b4f5cb5be5e9c91350abc",
      "c2ea389e91bc401da180a1083c6b66d6",
      "9b9ce4bbea5944418d526b6d4e741bf2"
     ]
    },
    "outputId": "9e67b091-d27d-451d-cea2-f094a215b78c",
    "cell_id": "00011-8bc63366-1f32-43d5-af0e-fc4e41948fb7",
    "deepnote_cell_type": "code"
   },
   "source": "# Use the model for prediction\nprint(\n    model_fon.predict(\n        \n            Fon_test[\"French\"].values[25]\n        \n    )\n)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32159e862aba44f59e253150fefb9aea",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Generating outputs:   0%|          | 0/24 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3260: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n  FutureWarning,\n",
     "name": "stderr"
    },
    {
     "output_type": "stream",
     "text": "['Dɔnkpá', 'ɖokpó sɔ́', 'élɔ́ mɛ̀', 'xojlawemajlakpà', 'kɔkɔ́kɔ́ɖɔ́', 'gɔn ɛ́', 'ɖagbe', 'yi atín', 'gbɔ̀n gbɔ̀n', 'ɖokpo', 'hălù', 'ɔ́, alo wŭ', 'asì - asɔ ɔ́', 'nyì lanmὲ', 'ɖù nù nùsáwŭ', 'nu e', 'Ffrika wá', 'é ɔ́, ɖò hŭn yì', 'wàn', 'ɖo ɛ̀', 'ɖĕbŭ', 'wè', 'é nyɔ́ nú mì', 'ɖe jijɔ']\n",
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Predicting `FONGBE` Test Data",
   "metadata": {
    "tags": [],
    "cell_id": "00020-930f8260-13c8-45a0-a509-d352eea4f69c",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "trusted": true,
    "id": "s-i1E6vKsBBz",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 205,
     "referenced_widgets": [
      "b966f494529c4884a5f72a5ae20f2eff",
      "16555fcebe4a47468c4531fbdc3be835",
      "3dc4ca553c73487d8bd7078e79081ba1",
      "b91ff04455294433be7ef51fa2ab5cc1",
      "099a2077dd524b9ea644f955e79c2025",
      "8d2a4694481641ee90f1e6e8b86378cc",
      "caa5428916654e05a57fd6a2fddf0220",
      "175e6c49fa4844588cb55a0b0e44fbb8",
      "c4a8d377cd6a4141aba946f1503dbec7",
      "54ba865269b24938a965f88b51b05828",
      "874b56a60df9476798335242f1b0bd95"
     ]
    },
    "outputId": "d507af14-9978-4be8-d584-4aeb0d43f4b0",
    "cell_id": "00012-c5f41b2d-1856-4221-9be1-d24d35dabe76",
    "deepnote_cell_type": "code"
   },
   "source": "Fon_test[\"Target\"] = model_fon.predict(list(Fon_test[\"French\"].values))",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b966f494529c4884a5f72a5ae20f2eff",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Generating outputs:   0%|          | 0/367 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3260: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n  FutureWarning,\n/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  \"\"\"Entry point for launching an IPython kernel.\n",
     "name": "stderr"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Saving csv file",
   "metadata": {
    "tags": [],
    "cell_id": "00022-d8d693bd-cc74-45f7-98c8-65bfcc194e08",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "trusted": true,
    "id": "jN4oH9-6sBB0",
    "cell_id": "00013-e4e25995-8a54-40a0-8b27-6442fd6001c2",
    "deepnote_cell_type": "code"
   },
   "source": "Fon_test[[\"ID\",\"Target\"]].to_csv(\"Fon.csv\",index=False)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Saving the model",
   "metadata": {
    "tags": [],
    "cell_id": "00023-5e453138-c5a3-4d1c-973c-f0604f94fea0",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "ka1VZWKcEi3R",
    "cell_id": "00014-ee198b66-9c1b-48d6-b247-8731cdc1f81a",
    "deepnote_cell_type": "code"
   },
   "source": "import torch",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "trusted": true,
    "id": "JNhHJYqlsBB0",
    "cell_id": "00015-daaff912-4030-4d4a-a5e0-77167d8371d1",
    "deepnote_cell_type": "code"
   },
   "source": "torch.save(model_fon , 'model_fon.pkl')",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "# Training `EWE` Model\nUsing simple transformer seq2seq I have downloaded `Helsinki-NLP/opus-mt-en-mul` which work best in our case and using specific `Seq2SeqArgs` to set arguments of model.\n\n**Args**\n- num_train_epochs = 30\n- batch_size = 32\n- max_length = 120\n- src_lang =\"fr\"\n- tgt_lang =\"ewe\"\n- overwrite_output_dir = True",
   "metadata": {
    "id": "ng1yJoTYsBB1",
    "cell_id": "00016-b7e11361-d6f3-4183-af89-9baeb05c707a",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "trusted": true,
    "id": "wr43cBnGsBB2",
    "cell_id": "00017-c2a34956-e50f-436e-9ada-a84daad6210f",
    "deepnote_cell_type": "code"
   },
   "source": "import logging\n\nimport pandas as pd\nfrom simpletransformers.seq2seq import (\n    Seq2SeqModel,\n    Seq2SeqArgs,\n)\n\n\nlogging.basicConfig(level=logging.INFO)\ntransformers_logger = logging.getLogger(\"transformers\")\ntransformers_logger.setLevel(logging.WARNING)\ntrain_data = Ewe[[\"French\",\"Target\"]]\n\ntrain_data = train_data.rename(columns={\"French\":\"input_text\",\"Target\":\"target_text\"})\ntrain_df, eval_df = train_test_split(train_data, test_size=0.20, random_state=42)\n\n\nmodel_args = Seq2SeqArgs()\nmodel_args.num_train_epochs = 30\nmodel_args.no_save = True\nmodel_args.evaluate_generated_text = False\nmodel_args.evaluate_during_training = False\nmodel_args.evaluate_during_training_verbose = True\nmodel_args.rag_embed_batch_size = 32\nmodel_args.max_length = 120\nmodel_args.src_lang =\"fr\"\nmodel_args.tgt_lang =\"ewe\"\nmodel_args.overwrite_output_dir = True\n# Initialize model\nmodel_ewe = Seq2SeqModel(\n    encoder_decoder_type=\"marian\",\n    encoder_decoder_name=\"Helsinki-NLP/opus-mt-en-mul\",\n    args=model_args,\n    use_cuda=True,\n)\n",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "NxOAoV3daELw",
    "cell_id": "00018-43dab7f5-34fd-460f-a741-9cc19af4c681",
    "deepnote_cell_type": "code"
   },
   "source": "\ndef count_matches(labels, preds):\n    print(labels)\n    print(preds)\n    return sum(\n        [\n            1 if label == pred else 0\n            for label, pred in zip(labels, preds)\n        ]\n    )",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Training Model",
   "metadata": {
    "tags": [],
    "cell_id": "00029-bf2b5017-f6d5-4186-906a-b008dab339a4",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "trusted": true,
    "id": "7n-qG-VgsBB3",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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    },
    "outputId": "c4263b00-16b4-4638-9ed5-700d3ad3905c",
    "cell_id": "00019-77d912a3-5b89-43c2-92f4-52d6a65e627d",
    "deepnote_cell_type": "code"
   },
   "source": "model_ewe.train_model(\n    train_df, eval_data=eval_df, matches=count_matches\n)",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_utils: Creating features from dataset file at cache_dir/\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5164b0072b3f4ad99d104a7526c86c1a",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "  0%|          | 0/8261 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_model: Training started\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88ef6d2d11f2418dbbe7e0dc3f2d7799",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Epoch:   0%|          | 0/30 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f82779cfaecd46abba97057d046a2459",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Running Epoch 0 of 30:   0%|          | 0/1033 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-1033-epoch-1\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6863b36e07343c6bb115002988d17db",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Running Epoch 1 of 30:   0%|          | 0/1033 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "INFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-2000\nINFO:simpletransformers.seq2seq.seq2seq_model:Saving model into outputs/checkpoint-2066-epoch-2\n",
     "name": "stderr"
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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    },
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    {
     "output_type": "stream",
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     "name": "stderr"
    },
    {
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     "data": {
      "text/plain": "(30990, 1.3801951767043092)"
     },
     "metadata": {
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     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Predicting on `EWE` Test Dataset",
   "metadata": {
    "tags": [],
    "cell_id": "00031-0d51d33a-b713-441f-92db-8b7f2ace2aef",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "trusted": true,
    "id": "vHnhWFKrsBB4",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 205,
     "referenced_widgets": [
      "1d3b35d22ffe4d3e9e7214ce212eaa3c",
      "e8f4c1646e924f459cca1d7886ac7fc9",
      "a3ca155c8f194c3895e9604d91ea2bdd",
      "696fb27a8f24430e90a0052efd03b266",
      "2e2eb97e061a45ff9b1cbf7d567b564c",
      "7c39b0140a09421b84ffeaea1f1280d7",
      "c8052886bf3d40058b885411599ce209",
      "b57f7abc3b2048ce9d53256349e99790",
      "092be692246641f084cd031e6d01c3e3",
      "5d72c974de654cd99f027088f86b5d1b",
      "2e694e0b16d743908d60b7b50b8521fc"
     ]
    },
    "outputId": "3d493605-2760-4dc4-bd6e-64d79c229458",
    "cell_id": "00020-98d4833c-38ac-4c4a-b82b-77d11c486744",
    "deepnote_cell_type": "code"
   },
   "source": "Ewe_test[\"Target\"]=model_ewe.predict(list(Ewe_test[\"French\"].values))",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d3b35d22ffe4d3e9e7214ce212eaa3c",
       "version_minor": 0,
       "version_major": 2
      },
      "text/plain": "Generating outputs:   0%|          | 0/371 [00:00<?, ?it/s]"
     },
     "metadata": {
      "tags": []
     }
    },
    {
     "output_type": "stream",
     "text": "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:3260: FutureWarning: `prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` context manager to prepare your targets. See the documentation of your specific tokenizer for more details\n  FutureWarning,\n/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  \"\"\"Entry point for launching an IPython kernel.\n",
     "name": "stderr"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Saving into csv file",
   "metadata": {
    "tags": [],
    "cell_id": "00033-3cac45a1-9df8-46bc-8cff-6fdeebf9a485",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "trusted": true,
    "id": "SyvK8-tXsBB5",
    "cell_id": "00021-6184712f-76c4-4bec-9236-60f60a28712a",
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   "source": "Ewe_test[[\"ID\",\"Target\"]].to_csv(\"Ewe.csv\",index=False)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Saving Model",
   "metadata": {
    "tags": [],
    "cell_id": "00036-04bc8b62-9fd3-447b-b7ba-97fe6db3cf14",
    "deepnote_cell_type": "markdown"
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  {
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   "metadata": {
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    "id": "iHD-gpDpsBB8",
    "cell_id": "00022-1dbd42e2-6bed-4ff3-bd1a-52545f38c79a",
    "deepnote_cell_type": "code"
   },
   "source": "torch.save(model_ewe , 'model_ewe.pkl')",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "# Submission\nconcating both predicted data sets",
   "metadata": {
    "tags": [],
    "cell_id": "00038-218a470d-6c4f-4bc0-b5f6-e6e0b43ed062",
    "deepnote_cell_type": "markdown"
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    "cell_id": "00023-62921bee-5d42-45ff-92a6-2d6c3e540956",
    "deepnote_cell_type": "code"
   },
   "source": "ewe = pd.read_csv('Ewe.csv')\nfon = pd.read_csv('Fon.csv')\nfr_to_targ_lang_sub = pd.concat([ewe, fon])\nfr_to_targ_lang_sub.head()",
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>Target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ID_AAAAhgRX</td>\n      <td>eƒe vɔvɔ̃ hã</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ID_AChdWHyF</td>\n      <td>nyigba gbɔgbɔ</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ID_AHBSoUNL</td>\n      <td>ele abe esia ene... » » Basa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ID_AHycIkQv</td>\n      <td>xlẽa nu vɔ̃ sɔgbɔ</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ID_AIWTdKBT</td>\n      <td>ɖeka be akpɔ akplasi kple adzreɖeɖeame ɖe ta, ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "            ID                                             Target\n0  ID_AAAAhgRX                                       eƒe vɔvɔ̃ hã\n1  ID_AChdWHyF                                      nyigba gbɔgbɔ\n2  ID_AHBSoUNL                       ele abe esia ene... » » Basa\n3  ID_AHycIkQv                                  xlẽa nu vɔ̃ sɔgbɔ\n4  ID_AIWTdKBT  ɖeka be akpɔ akplasi kple adzreɖeɖeame ɖe ta, ..."
     },
     "metadata": {
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     },
     "execution_count": 10
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Submission file `FON and EWE`",
   "metadata": {
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   "source": "fr_to_targ_lang_sub.to_csv('/content/drive/MyDrive/Fon_and_ewe/submission1.csv', index=False)",
   "execution_count": null,
   "outputs": []
  },
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   "cell_type": "markdown",
   "source": "# Results",
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