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+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
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+ "Installing collected packages: tokenizers, transformers\n",
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+ "Successfully installed tokenizers-0.13.3 transformers-4.28.1\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "!pip install datasets\n",
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+ "!pip install transformers\n",
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+ "import pandas as pd\n",
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+ "from sklearn.model_selection import train_test_split\n",
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+ "import numpy as np\n",
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+ "import transformers\n",
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+ "import torch\n",
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+ "import csv\n",
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+ "from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler\n",
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+ "from transformers import DistilBertTokenizer, DistilBertModel, DistilBertForSequenceClassification, AdamW"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "filename = \"/content/sample_data/train.csv\"\n",
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+ "df = pd.read_csv(filename)\n",
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+ "df.head()\n",
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+ "df.drop(['id'], inplace=True, axis=1)\n",
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+ "newdf = pd.DataFrame()\n",
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+ "newdf['text'] = df['comment_text']\n",
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+ "newdf['labels'] = df.iloc[:, 1:].values.tolist()\n",
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+ "\n",
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+ "newdf.head()"
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+ ],
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 381
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+ },
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+ "id": "XQEDvn-7ksXU",
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+ "outputId": "960bd74f-2533-4eab-9800-643823e14f2f"
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+ },
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+ "execution_count": null,
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+ "outputs": [
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+ {
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+ "output_type": "error",
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+ "ename": "FileNotFoundError",
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+ "evalue": "ignored",
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+ "traceback": [
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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+ "\u001b[0;32m<ipython-input-3-dadeecb77ee2>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/sample_data/train.csv\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mnewdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
156
+ "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
157
+ "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 330\u001b[0m )\n\u001b[0;32m--> 331\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
158
+ "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
159
+ "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 604\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 607\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
160
+ "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1441\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1444\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
161
+ "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m self.handles = get_handle(\n\u001b[0m\u001b[1;32m 1736\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
162
+ "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 855\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 857\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
163
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/sample_data/train.csv'"
164
+ ]
165
+ }
166
+ ]
167
+ },
168
+ {
169
+ "cell_type": "code",
170
+ "source": [
171
+ "epoch = 1\n",
172
+ "max_len = 128\n",
173
+ "batch_size = 5\n",
174
+ "\n",
175
+ "train_df, val_df = train_test_split(newdf, test_size=0.2, random_state=42)\n",
176
+ "\"\"\"\n",
177
+ "DistilBertTokenizer\n",
178
+ "torch.utils.data.Dataset\n",
179
+ "inputs = self.tokenizer.encode_plus\n",
180
+ "DataLoader\n",
181
+ "PreTrainedModel\n",
182
+ "DistilBertForSequenceClassification\n",
183
+ "DistilBertConfig\n",
184
+ "model = DistilBertClassifier2(config)\n",
185
+ "model.to(device)\n",
186
+ "torch.optim.Adam\n",
187
+ "tokenizer.encode_plus\n",
188
+ "tokenizer.save_pretrained(\"model\")\n",
189
+ "model.save_pretrained(\"model\")\n",
190
+ "\"\"\""
191
+ ],
192
+ "metadata": {
193
+ "colab": {
194
+ "base_uri": "https://localhost:8080/",
195
+ "height": 235
196
+ },
197
+ "id": "DO8fKxgnwIPz",
198
+ "outputId": "d0f73814-62a0-4d74-9353-3d4ce90b6d1b"
199
+ },
200
+ "execution_count": null,
201
+ "outputs": [
202
+ {
203
+ "output_type": "error",
204
+ "ename": "NameError",
205
+ "evalue": "ignored",
206
+ "traceback": [
207
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
208
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
209
+ "\u001b[0;32m<ipython-input-4-232584db16d1>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtrain_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \"\"\"\n\u001b[1;32m 7\u001b[0m \u001b[0mDistilBertTokenizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
210
+ "\u001b[0;31mNameError\u001b[0m: name 'newdf' is not defined"
211
+ ]
212
+ }
213
+ ]
214
+ },
215
+ {
216
+ "cell_type": "code",
217
+ "source": [
218
+ "class DS(Dataset):\n",
219
+ " def __init__(self, dataframe, max_len):\n",
220
+ " self.data = dataframe\n",
221
+ " self.max_len = max_len\n",
222
+ " self.text = dataframe.text\n",
223
+ " self.targets = self.data.labels\n",
224
+ " self.tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
225
+ " \n",
226
+ " def __len__(self):\n",
227
+ " return len(self.data)\n",
228
+ " \n",
229
+ " def __getitem__(self, index):\n",
230
+ " text = str(self.text.iloc[index])\n",
231
+ " text = \" \".join(text.split())\n",
232
+ "\n",
233
+ " inputs = self.tokenizer.encode_plus(\n",
234
+ " text, None,\n",
235
+ " add_special_tokens=True,\n",
236
+ " max_length=self.max_len,\n",
237
+ " pad_to_max_length=True,\n",
238
+ " return_token_type_ids=True)\n",
239
+ " ids = inputs['input_ids']\n",
240
+ " mask = inputs['attention_mask']\n",
241
+ " token_type_ids = inputs[\"token_type_ids\"]\n",
242
+ " return {\n",
243
+ " 'ids': torch.tensor(ids, dtype=torch.long),\n",
244
+ " 'attention_mask': torch.tensor(mask, dtype=torch.long),\n",
245
+ " 'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),\n",
246
+ " 'labels': torch.tensor(self.targets.iloc[index], dtype=torch.float)\n",
247
+ " }\n"
248
+ ],
249
+ "metadata": {
250
+ "id": "i80qLafpzWDh"
251
+ },
252
+ "execution_count": null,
253
+ "outputs": []
254
+ },
255
+ {
256
+ "cell_type": "code",
257
+ "source": [
258
+ "traindata = DS(train_df, max_len)\n",
259
+ "validdata = DS(val_df, max_len)\n",
260
+ "train_loader = DataLoader(traindata, batch_size=batch_size, shuffle=True)\n",
261
+ "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n"
262
+ ],
263
+ "metadata": {
264
+ "id": "EMScGH58Poaw",
265
+ "colab": {
266
+ "base_uri": "https://localhost:8080/",
267
+ "height": 201
268
+ },
269
+ "outputId": "de081257-fb6c-4c73-c54d-e88dd1e2603f"
270
+ },
271
+ "execution_count": null,
272
+ "outputs": [
273
+ {
274
+ "output_type": "error",
275
+ "ename": "NameError",
276
+ "evalue": "ignored",
277
+ "traceback": [
278
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
279
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
280
+ "\u001b[0;32m<ipython-input-6-84bc2329fae8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtraindata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mvaliddata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDS\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtrain_loader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraindata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
281
+ "\u001b[0;31mNameError\u001b[0m: name 'train_df' is not defined"
282
+ ]
283
+ }
284
+ ]
285
+ },
286
+ {
287
+ "cell_type": "code",
288
+ "metadata": {
289
+ "id": "fb9-Yr9YDZqo",
290
+ "colab": {
291
+ "base_uri": "https://localhost:8080/",
292
+ "height": 235
293
+ },
294
+ "outputId": "0664e5d0-55cb-4b58-e75a-b9acdab82e73"
295
+ },
296
+ "source": [
297
+ "device = torch.device('cuda')\n",
298
+ "\n",
299
+ "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=6, problem_type=\"multi_label_classification\")\n",
300
+ "model.to(device)\n",
301
+ "model.train()\n",
302
+ "\n",
303
+ "optimizer = AdamW(model.parameters(), lr=5e-5)\n"
304
+ ],
305
+ "execution_count": null,
306
+ "outputs": [
307
+ {
308
+ "output_type": "error",
309
+ "ename": "NameError",
310
+ "evalue": "ignored",
311
+ "traceback": [
312
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
313
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
314
+ "\u001b[0;32m<ipython-input-1-26b16f11a588>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cuda'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDistilBertForSequenceClassification\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'distilbert-base-uncased'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproblem_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"multi_label_classification\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
315
+ "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
316
+ ]
317
+ }
318
+ ]
319
+ },
320
+ {
321
+ "cell_type": "code",
322
+ "source": [
323
+ "for i in range(epoch):\n",
324
+ " for batch in train_loader:\n",
325
+ " optimizer.zero_grad()\n",
326
+ " ids = batch['input_ids'].to(device)\n",
327
+ " mask = batch['attention_mask'].to(device)\n",
328
+ " labels = batch['labels'].to(device)\n",
329
+ "\n",
330
+ " outputs = model(ids, attention_mask=mask, labels=labels)\n",
331
+ "\n",
332
+ " loss = outputs[0]\n",
333
+ " loss.backward()\n",
334
+ " optimizer.step()\n",
335
+ "model.eval()\n"
336
+ ],
337
+ "metadata": {
338
+ "id": "mtMhE5_z8kw8"
339
+ },
340
+ "execution_count": null,
341
+ "outputs": []
342
+ },
343
+ {
344
+ "cell_type": "code",
345
+ "source": [
346
+ "xtrain = [\"FUCK YOUR FILTHY MOTHER IN THE ASS, DRY!\"]\n",
347
+ "batch = tokenizer(xtrain, truncation=True, padding='max_length', return_tensors=\"pt\").to(device)\n",
348
+ "\n",
349
+ "with torch.no_grad():\n",
350
+ " outputs = model(**batch)\n",
351
+ " results = torch.sigmoid(outputs.logits)*100\n",
352
+ " print(results)\n",
353
+ "\n",
354
+ "model.save_pretrained(\"pretrained_model\")\n",
355
+ "tokenizer.save_pretrained(\"model_tokenizer\")"
356
+ ],
357
+ "metadata": {
358
+ "id": "8T4UG8K8BvUn"
359
+ },
360
+ "execution_count": null,
361
+ "outputs": []
362
+ }
363
+ ]
364
+ }