simple-transformer-yoruba-mt.ipynb

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   "source": "# Machine translation using `simpletransformers` *Yorùbá to English*\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-20210601-123125.png)",
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  {
   "cell_type": "markdown",
   "source": "## Notebook was made in Kaggle",
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   "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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  {
   "cell_type": "markdown",
   "source": "# Data loading\nI have used original data with no preprocessing ",
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   "source": "import pandas as pd\nfrom sklearn.model_selection import train_test_split\n\ntrain = pd.read_csv(\"../input/yorb-machine-translation/Train.csv\")\ntest = pd.read_csv(\"../input/yorb-machine-translation/Test.csv\")",
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   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "# `Yoruba` Model\nUsing simple transformer seq2seq I have downloaded `Helsinki-NLP/opus-mt-mul-en` which work best in our case and using specific `Seq2SeqArgs` to set arguments of model.\n\n**Args**\n- num_train_epochs = 35\n- batch_size = 32\n- max_length = 120\n- src_lang =\"yor\"\n- tgt_lang =\"en_XX\"",
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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 = train[[\"Yoruba\",\"English\"]]\n\ntrain_data = train_data.rename(columns={\"Yoruba\":\"input_text\",\"English\":\"target_text\"})\ntrain_df, eval_df = train_test_split(train_data, test_size=0.05, random_state=42)\n\n\nmodel_args = Seq2SeqArgs()\nmodel_args.num_train_epochs = 35\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 =\"yor\"\nmodel_args.tgt_lang =\"en_XX\"\n\n# Initialize model\nmodel = Seq2SeqModel(\n    encoder_decoder_type=\"marian\",\n    encoder_decoder_name=\"Helsinki-NLP/opus-mt-mul-en\",\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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   "cell_type": "markdown",
   "source": "## Model Training",
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   },
   "source": "# Train the model\nmodel.train_model(\n    train_df, eval_data=eval_df, matches=count_matches\n)\n",
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     "text": "/opt/conda/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:136: 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)\n"
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be92800b748347bba5ec14ae4370180c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 18 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d336b6c122ac441e855234ccff823243",
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       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 19 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72f3850b4ea94d8ab868abeb6a91f679",
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       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 20 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1fd40702467e4240b9b2d61cad5e34e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 21 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06c597747ca24c119dc80045f074a969",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 22 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a6b9521c47a45f497136606bec7d0c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 23 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0574b95dda9d456aa1df07c4b1674f56",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 24 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc2882d622cf471ca8afd50a79bc5f26",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 25 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cbb6bc77587245089eb9a6304221fdf7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 26 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f06987e7ae44507b24115397bfb32e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 27 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90c45cd1ff9447bdbeedbc33b6baf718",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 28 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e8a40f930d3048859b5d7a2dac43a7e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 29 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9dfb4f9306674c8487a64f63fd0fb8d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 30 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca6674639a3c4d41a25edf49a6f62c4e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 31 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4515511db3974bab84d57b9205c041a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 32 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d3acf2943e549689dc91455254a2158",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 33 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e58996a5f5f74807aec48c818087bbc2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Epoch 34 of 35', max=1194.0, style=ProgressStyle(…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n\n"
    },
    {
     "data": {
      "text/plain": "(41790, 0.9875997785191789)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Model Evaluation",
   "metadata": {
    "tags": [],
    "cell_id": "00011-15e12781-2a1b-436c-b909-e36378ad0d76",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-29T20:45:05.027392Z",
     "iopub.status.busy": "2021-05-29T20:45:05.026378Z",
     "iopub.status.idle": "2021-05-29T20:45:10.285876Z",
     "shell.execute_reply": "2021-05-29T20:45:10.286785Z"
    },
    "papermill": {
     "duration": 5.354427,
     "end_time": "2021-05-29T20:45:10.287063",
     "exception": false,
     "start_time": "2021-05-29T20:45:04.932636",
     "status": "completed"
    },
    "tags": [],
    "cell_id": "00004-5f9356cd-1e84-4261-b3c0-6eb573d4baa5",
    "deepnote_cell_type": "code"
   },
   "source": "# # Evaluate the model\nresults = model.eval_model(eval_df)",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81b95c7d0cc94ef789ecc9ecded3dd4a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, max=503.0), HTML(value='')))"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b52e4dee3bee4a85b7983ae43574934b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Running Evaluation', max=63.0, style=ProgressStyle(descri…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-29T20:45:10.838040Z",
     "iopub.status.busy": "2021-05-29T20:45:10.837095Z",
     "iopub.status.idle": "2021-05-29T20:45:10.863459Z",
     "shell.execute_reply": "2021-05-29T20:45:10.857389Z"
    },
    "papermill": {
     "duration": 0.35149,
     "end_time": "2021-05-29T20:45:10.863656",
     "exception": false,
     "start_time": "2021-05-29T20:45:10.512166",
     "status": "completed"
    },
    "tags": [],
    "cell_id": "00005-20639f96-387e-40f4-89d7-a77d6bfda8af",
    "deepnote_cell_type": "code"
   },
   "source": "results",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "{'eval_loss': 2.804287210343376}"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Sample Prediction",
   "metadata": {
    "tags": [],
    "cell_id": "00014-d1dfc165-523d-4b2b-871b-a17b81af11bc",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-29T20:45:11.324828Z",
     "iopub.status.busy": "2021-05-29T20:45:11.323765Z",
     "iopub.status.idle": "2021-05-29T20:45:11.852154Z",
     "shell.execute_reply": "2021-05-29T20:45:11.852678Z"
    },
    "papermill": {
     "duration": 0.694661,
     "end_time": "2021-05-29T20:45:11.852856",
     "exception": false,
     "start_time": "2021-05-29T20:45:11.158195",
     "status": "completed"
    },
    "tags": [],
    "cell_id": "00006-a225bbc9-a866-4b16-9f84-3f7c4d0ba467",
    "deepnote_cell_type": "code"
   },
   "source": "# Use the model for prediction\nprint(\n    model.predict(\n        \n            test.Yoruba.values[25]\n        \n    )\n)",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1aafc17f771a47fdbd28319b8866af5d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Generating outputs', max=6.0, style=ProgressStyle(descrip…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "/opt/conda/lib/python3.7/site-packages/transformers/tokenization_utils_base.py:3226: 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": "stdout",
     "output_type": "stream",
     "text": "\n['Mother! Oh!', 'put it in', 'over-What', 'Sunday', 'and next', 'e. E.,']\n"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Predicting English from Yoruba `Test`",
   "metadata": {
    "tags": [],
    "cell_id": "00016-357c2967-616f-4734-b641-a35143a3594f",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-29T20:45:12.049971Z",
     "iopub.status.busy": "2021-05-29T20:45:12.048851Z",
     "iopub.status.idle": "2021-05-29T20:59:49.493288Z",
     "shell.execute_reply": "2021-05-29T20:59:49.492685Z",
     "shell.execute_reply.started": "2021-05-26T14:54:37.528373Z"
    },
    "papermill": {
     "duration": 877.545277,
     "end_time": "2021-05-29T20:59:49.493448",
     "exception": false,
     "start_time": "2021-05-29T20:45:11.948171",
     "status": "completed"
    },
    "tags": [],
    "cell_id": "00007-5add486e-8751-4c2e-9f2a-fd0bf60369f0",
    "deepnote_cell_type": "code"
   },
   "source": "test[\"Label\"] = model.predict(list(test.Yoruba.values))",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b08e949103248ef84eb8be94477067a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": "HBox(children=(FloatProgress(value=0.0, description='Generating outputs', max=852.0, style=ProgressStyle(descr…"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": "\n"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-29T20:59:49.695478Z",
     "iopub.status.busy": "2021-05-29T20:59:49.694773Z",
     "iopub.status.idle": "2021-05-29T20:59:49.707751Z",
     "shell.execute_reply": "2021-05-29T20:59:49.706921Z"
    },
    "papermill": {
     "duration": 0.120462,
     "end_time": "2021-05-29T20:59:49.707910",
     "exception": false,
     "start_time": "2021-05-29T20:59:49.587448",
     "status": "completed"
    },
    "tags": [],
    "cell_id": "00008-5d2a1aa4-8a06-4ee9-ad62-0f0d61ae9c8f",
    "deepnote_cell_type": "code"
   },
   "source": "test.head()",
   "execution_count": null,
   "outputs": [
    {
     "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>Yoruba</th>\n      <th>Label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ID_AAAitMaH</td>\n      <td>Nínú ìpè kan lẹ́yìn ìgbà náà, wọ́n sọ fún aṣoj...</td>\n      <td>In a later call, the BlaBlaBlacar▁representati...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ID_AAKKdQwr</td>\n      <td>Nítorí kò sí nǹkan tí ọkùnrin ò lè ṣe láì náán...</td>\n      <td>Because there is nothing that a man cannot do ...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ID_ABgAyEOp</td>\n      <td>Bí i kó pariwo. Kí ó kígbe mọ́ ẹ?</td>\n      <td>Because he would sing a noise. Why would he li...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ID_ACFgfKQs</td>\n      <td>Tí ó ń lé e lọ sọ́nà etí odò Akókurà, tí ó bẹ̀...</td>\n      <td>While▁following, he goes to the Akokurà River,...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ID_ACNPmlhf</td>\n      <td>Èṣúńiyì mọ̀ iṣẹ́ rẹ̀ dunjú. Màmá tirí bí ó ṣe ...</td>\n      <td>Eswuniy knows his job. Mother is called on how...</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "            ID                                             Yoruba  \\\n0  ID_AAAitMaH  Nínú ìpè kan lẹ́yìn ìgbà náà, wọ́n sọ fún aṣoj...   \n1  ID_AAKKdQwr  Nítorí kò sí nǹkan tí ọkùnrin ò lè ṣe láì náán...   \n2  ID_ABgAyEOp                  Bí i kó pariwo. Kí ó kígbe mọ́ ẹ?   \n3  ID_ACFgfKQs  Tí ó ń lé e lọ sọ́nà etí odò Akókurà, tí ó bẹ̀...   \n4  ID_ACNPmlhf  Èṣúńiyì mọ̀ iṣẹ́ rẹ̀ dunjú. Màmá tirí bí ó ṣe ...   \n\n                                               Label  \n0  In a later call, the BlaBlaBlacar▁representati...  \n1  Because there is nothing that a man cannot do ...  \n2  Because he would sing a noise. Why would he li...  \n3  While▁following, he goes to the Akokurà River,...  \n4  Eswuniy knows his job. Mother is called on how...  "
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "# Submission",
   "metadata": {
    "tags": [],
    "cell_id": "00019-ba1d8ca8-dd6e-4d37-b726-6718363ff4ec",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-29T20:59:49.904864Z",
     "iopub.status.busy": "2021-05-29T20:59:49.903753Z",
     "iopub.status.idle": "2021-05-29T20:59:49.954574Z",
     "shell.execute_reply": "2021-05-29T20:59:49.953961Z",
     "shell.execute_reply.started": "2021-05-26T14:46:49.174747Z"
    },
    "papermill": {
     "duration": 0.15224,
     "end_time": "2021-05-29T20:59:49.954742",
     "exception": false,
     "start_time": "2021-05-29T20:59:49.802502",
     "status": "completed"
    },
    "tags": [],
    "cell_id": "00009-1c0eed3c-01b1-4761-bdaf-3c7d55bbd007",
    "deepnote_cell_type": "code"
   },
   "source": "test[[\"ID\",\"Label\"]].to_csv(\"submission.csv\",index=False)",
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "# Results",
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