Początek

seq_labeling.py.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os.path\n",
+    "import gzip\n",
+    "import shutil\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.isfile('train/train.tsv'):\n",
+    "    import lzma\n",
+    "    with lzma.open('train/train.tsv.xz', 'rb') as f_in:\n",
+    "        with open('train/train.tsv', 'wb') as f_out:\n",
+    "            shutil.copyfileobj(f_in, f_out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv('train/train.tsv', sep='\\t', names=['labels', 'text'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "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>Label</th>\n",
+       "      <th>Word</th>\n",
+       "      <th>WordLen</th>\n",
+       "      <th>WordHasDigit</th>\n",
+       "      <th>CapitalFirst</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Label, Word, WordLen, WordHasDigit, CapitalFirst]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = []\n",
+    "for sentence in raw_data.to_numpy():\n",
+    "    for label, word in zip(sentence[0].split(), sentence[1].split()):\n",
+    "        data.append([label,word,len(word), any(c.isdigit() for c in word), word.isupper()])\n",
+    "df = pd.DataFrame(data, columns=['Label', 'Word', 'WordLen', 'WordHasDigit', 'CapitalFirst'], index=None)\n",
+    "df[df[\"Label\"]==None]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def labels_process(dt):\n",
+    "    return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]\n",
+    "\n",
+    "def data_process(dt):\n",
+    "    return [ torch.tensor([vocab['<bos>']] +[vocab[token]  for token in  document ] + [vocab['<eos>']], dtype = torch.long) for document in dt]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}