Text Extensions for Pandas: Tips and Techniques for Extending Pandas, Part 1 Blog

tep_extending_pandas_blog1_3.ipynb

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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token_id</th>\n",
       "      <th>span</th>\n",
       "      <th>input_id</th>\n",
       "      <th>token_type_id</th>\n",
       "      <th>attention_mask</th>\n",
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       "      <th>embedding</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>[0, 5): 'Monty'</td>\n",
       "      <td>18446</td>\n",
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       "      <td>[     0.8781024,     0.16891398,    -0.0992790...</td>\n",
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       "      <td>[6, 12): 'Python'</td>\n",
       "      <td>18750</td>\n",
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       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>[    0.44240445,     0.97607553,   0.002306254...</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>[13, 16): 'and'</td>\n",
       "      <td>1998</td>\n",
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       "      <td>[    -0.8111421,     0.61899036,      -0.64434...</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>[17, 20): 'the'</td>\n",
       "      <td>1996</td>\n",
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       "      <td>[    -0.3957557,     0.51493996,     -0.646472...</td>\n",
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       "      <td>[21, 25): 'Holy'</td>\n",
       "      <td>4151</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>[     1.4014304,     0.68754816,     0.2479123...</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>[26, 28): 'Gr'</td>\n",
       "      <td>24665</td>\n",
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       "      <td>False</td>\n",
       "      <td>[  -0.084513076,     0.33457386,     0.2393767...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>[28, 31): 'ail'</td>\n",
       "      <td>12502</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>[      0.304468,     0.19518888,    -0.7640254...</td>\n",
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      "text/plain": [
       "   token_id               span  input_id  token_type_id  attention_mask  \\\n",
       "1         1    [0, 5): 'Monty'     18446              0               1   \n",
       "2         2  [6, 12): 'Python'     18750              0               1   \n",
       "3         3    [13, 16): 'and'      1998              0               1   \n",
       "4         4    [17, 20): 'the'      1996              0               1   \n",
       "5         5   [21, 25): 'Holy'      4151              0               1   \n",
       "6         6     [26, 28): 'Gr'     24665              0               1   \n",
       "7         7    [28, 31): 'ail'     12502              0               1   \n",
       "\n",
       "   special_tokens_mask                                          embedding  \n",
       "1                False  [     0.8781024,     0.16891398,    -0.0992790...  \n",
       "2                False  [    0.44240445,     0.97607553,   0.002306254...  \n",
       "3                False  [    -0.8111421,     0.61899036,      -0.64434...  \n",
       "4                False  [    -0.3957557,     0.51493996,     -0.646472...  \n",
       "5                False  [     1.4014304,     0.68754816,     0.2479123...  \n",
       "6                False  [  -0.084513076,     0.33457386,     0.2393767...  \n",
       "7                False  [      0.304468,     0.19518888,    -0.7640254...  "
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   "source": [
    "# Taking the above DataFrame, select all records not flagged as a special token.\n",
    "result = df[~df.special_tokens_mask]\n",
    "result"
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