0-1-create-datasets.ipynb

{
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  {
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   "execution_count": 1,
   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_url = 'data/prepared/'\n",
    "ds_files = {\n",
    "    'train': ds_url + 'train-mlabel.tsv',\n",
    "    'val': ds_url + 'val-mlabel.tsv',\n",
    "    'val.zhihu': ds_url + 'zhihu-mlabel.tsv'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-10d4daad55925362\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /home/erfan/.cache/huggingface/datasets/csv/default-10d4daad55925362/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"
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    {
     "data": {
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       "model_id": "7ef68afff36946c1abeb1dad0b39c329",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "model_id": "69947365b1ec473987f4935c52de1590",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "3ad10398746f4d7e9899758075195df1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/erfan/.local/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4c7fb47de0d4f77882aead640dc8963",
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      },
      "text/plain": [
       "Generating val split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/erfan/.local/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42679c82c2d94791a7c9fd378bf68557",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating val.zhihu split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/erfan/.local/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
      "  return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /home/erfan/.cache/huggingface/datasets/csv/default-10d4daad55925362/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"
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    {
     "data": {
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       "model_id": "5e51eeac814c4fef94a5029d21e004cc",
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      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = load_dataset('csv', data_files=ds_files, delimiter='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['Argument ID', 'Text', 'Labels'],\n",
       "        num_rows: 5393\n",
       "    })\n",
       "    val: Dataset({\n",
       "        features: ['Argument ID', 'Text', 'Labels'],\n",
       "        num_rows: 1896\n",
       "    })\n",
       "    val.zhihu: Dataset({\n",
       "        features: ['Argument ID', 'Text', 'Labels'],\n",
       "        num_rows: 100\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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