Spaces:
Runtime error
Runtime error
Rachel1809
commited on
Commit
•
fc6b132
1
Parent(s):
017304c
Upload Toxic_comment_classification.ipynb
Browse files- Toxic_comment_classification.ipynb +1810 -0
Toxic_comment_classification.ipynb
ADDED
@@ -0,0 +1,1810 @@
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1 |
+
{
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2 |
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"cells": [
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3 |
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{
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4 |
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"cell_type": "code",
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5 |
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"execution_count": null,
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6 |
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"metadata": {
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7 |
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"colab": {
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8 |
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"base_uri": "https://localhost:8080/"
|
9 |
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},
|
10 |
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"id": "8DfEKlbt_TMI",
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11 |
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"outputId": "79666846-0691-490a-88b0-5f56f4769772"
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12 |
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},
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"outputs": [
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{
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15 |
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"output_type": "stream",
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17 |
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18 |
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19 |
+
]
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20 |
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}
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21 |
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],
|
22 |
+
"source": [
|
23 |
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"from google.colab import drive\n",
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24 |
+
"drive.mount('/content/drive/')"
|
25 |
+
],
|
26 |
+
"id": "8DfEKlbt_TMI"
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27 |
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},
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28 |
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{
|
29 |
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"cell_type": "markdown",
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"metadata": {
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31 |
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"id": "8c25705b"
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32 |
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},
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33 |
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"source": [
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34 |
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35 |
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],
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36 |
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"id": "8c25705b"
|
37 |
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},
|
38 |
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{
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39 |
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"cell_type": "code",
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40 |
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"execution_count": null,
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41 |
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"metadata": {
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"id": "5b07ecd3"
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43 |
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},
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"outputs": [],
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45 |
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"source": [
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47 |
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48 |
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49 |
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"id": "5b07ecd3"
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],
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" <th>159570</th>\n",
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" <td>fff46fc426af1f9a</td>\n",
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" <td>\"\\nAnd ... I really don't think you understand...</td>\n",
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264 |
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"</table>\n",
|
265 |
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"<p>159571 rows × 8 columns</p>\n",
|
266 |
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"</div>\n",
|
267 |
+
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-701bc9b7-727d-42d9-9139-de748ebf4501')\"\n",
|
268 |
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" title=\"Convert this dataframe to an interactive table.\"\n",
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" </button>\n",
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" \n",
|
278 |
+
" <style>\n",
|
279 |
+
" .colab-df-container {\n",
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280 |
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" display:flex;\n",
|
281 |
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" flex-wrap:wrap;\n",
|
282 |
+
" gap: 12px;\n",
|
283 |
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" }\n",
|
284 |
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"\n",
|
285 |
+
" .colab-df-convert {\n",
|
286 |
+
" background-color: #E8F0FE;\n",
|
287 |
+
" border: none;\n",
|
288 |
+
" border-radius: 50%;\n",
|
289 |
+
" cursor: pointer;\n",
|
290 |
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" display: none;\n",
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291 |
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" fill: #1967D2;\n",
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292 |
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" height: 32px;\n",
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" padding: 0 0 0 0;\n",
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294 |
+
" width: 32px;\n",
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295 |
+
" }\n",
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296 |
+
"\n",
|
297 |
+
" .colab-df-convert:hover {\n",
|
298 |
+
" background-color: #E2EBFA;\n",
|
299 |
+
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
300 |
+
" fill: #174EA6;\n",
|
301 |
+
" }\n",
|
302 |
+
"\n",
|
303 |
+
" [theme=dark] .colab-df-convert {\n",
|
304 |
+
" background-color: #3B4455;\n",
|
305 |
+
" fill: #D2E3FC;\n",
|
306 |
+
" }\n",
|
307 |
+
"\n",
|
308 |
+
" [theme=dark] .colab-df-convert:hover {\n",
|
309 |
+
" background-color: #434B5C;\n",
|
310 |
+
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
311 |
+
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
312 |
+
" fill: #FFFFFF;\n",
|
313 |
+
" }\n",
|
314 |
+
" </style>\n",
|
315 |
+
"\n",
|
316 |
+
" <script>\n",
|
317 |
+
" const buttonEl =\n",
|
318 |
+
" document.querySelector('#df-701bc9b7-727d-42d9-9139-de748ebf4501 button.colab-df-convert');\n",
|
319 |
+
" buttonEl.style.display =\n",
|
320 |
+
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
321 |
+
"\n",
|
322 |
+
" async function convertToInteractive(key) {\n",
|
323 |
+
" const element = document.querySelector('#df-701bc9b7-727d-42d9-9139-de748ebf4501');\n",
|
324 |
+
" const dataTable =\n",
|
325 |
+
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
326 |
+
" [key], {});\n",
|
327 |
+
" if (!dataTable) return;\n",
|
328 |
+
"\n",
|
329 |
+
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
330 |
+
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
331 |
+
" + ' to learn more about interactive tables.';\n",
|
332 |
+
" element.innerHTML = '';\n",
|
333 |
+
" dataTable['output_type'] = 'display_data';\n",
|
334 |
+
" await google.colab.output.renderOutput(dataTable, element);\n",
|
335 |
+
" const docLink = document.createElement('div');\n",
|
336 |
+
" docLink.innerHTML = docLinkHtml;\n",
|
337 |
+
" element.appendChild(docLink);\n",
|
338 |
+
" }\n",
|
339 |
+
" </script>\n",
|
340 |
+
" </div>\n",
|
341 |
+
" </div>\n",
|
342 |
+
" "
|
343 |
+
]
|
344 |
+
},
|
345 |
+
"metadata": {},
|
346 |
+
"execution_count": 4
|
347 |
+
}
|
348 |
+
],
|
349 |
+
"source": [
|
350 |
+
"df"
|
351 |
+
],
|
352 |
+
"id": "1be479a4"
|
353 |
+
},
|
354 |
+
{
|
355 |
+
"cell_type": "markdown",
|
356 |
+
"metadata": {
|
357 |
+
"id": "e352d92f"
|
358 |
+
},
|
359 |
+
"source": [
|
360 |
+
"# 2. Preprocessing"
|
361 |
+
],
|
362 |
+
"id": "e352d92f"
|
363 |
+
},
|
364 |
+
{
|
365 |
+
"cell_type": "markdown",
|
366 |
+
"metadata": {
|
367 |
+
"id": "dc5fe893"
|
368 |
+
},
|
369 |
+
"source": [
|
370 |
+
"## 2.1. Data overview"
|
371 |
+
],
|
372 |
+
"id": "dc5fe893"
|
373 |
+
},
|
374 |
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{
|
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"cell_type": "code",
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"execution_count": null,
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 424
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"outputId": "adb8a890-565d-4e5b-da14-d7f11db89735"
|
384 |
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},
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{
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"output_type": "execute_result",
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"data": {
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"\n",
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537 |
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|
538 |
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"</div>\n",
|
539 |
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|
540 |
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" title=\"Convert this dataframe to an interactive table.\"\n",
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541 |
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|
542 |
+
" \n",
|
543 |
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" width=\"24px\">\n",
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" </svg>\n",
|
548 |
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" </button>\n",
|
549 |
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" \n",
|
550 |
+
" <style>\n",
|
551 |
+
" .colab-df-container {\n",
|
552 |
+
" display:flex;\n",
|
553 |
+
" flex-wrap:wrap;\n",
|
554 |
+
" gap: 12px;\n",
|
555 |
+
" }\n",
|
556 |
+
"\n",
|
557 |
+
" .colab-df-convert {\n",
|
558 |
+
" background-color: #E8F0FE;\n",
|
559 |
+
" border: none;\n",
|
560 |
+
" border-radius: 50%;\n",
|
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" cursor: pointer;\n",
|
562 |
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|
563 |
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" fill: #1967D2;\n",
|
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|
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" padding: 0 0 0 0;\n",
|
566 |
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" width: 32px;\n",
|
567 |
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" }\n",
|
568 |
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"\n",
|
569 |
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" .colab-df-convert:hover {\n",
|
570 |
+
" background-color: #E2EBFA;\n",
|
571 |
+
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
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+
" fill: #174EA6;\n",
|
573 |
+
" }\n",
|
574 |
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"\n",
|
575 |
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" [theme=dark] .colab-df-convert {\n",
|
576 |
+
" background-color: #3B4455;\n",
|
577 |
+
" fill: #D2E3FC;\n",
|
578 |
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" }\n",
|
579 |
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"\n",
|
580 |
+
" [theme=dark] .colab-df-convert:hover {\n",
|
581 |
+
" background-color: #434B5C;\n",
|
582 |
+
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
583 |
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
584 |
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" fill: #FFFFFF;\n",
|
585 |
+
" }\n",
|
586 |
+
" </style>\n",
|
587 |
+
"\n",
|
588 |
+
" <script>\n",
|
589 |
+
" const buttonEl =\n",
|
590 |
+
" document.querySelector('#df-30c83a4a-ec86-4758-82b4-5a5e6df88b01 button.colab-df-convert');\n",
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"\n",
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594 |
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" async function convertToInteractive(key) {\n",
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595 |
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" const element = document.querySelector('#df-30c83a4a-ec86-4758-82b4-5a5e6df88b01');\n",
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596 |
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"\n",
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601 |
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" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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"</div>\n",
|
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|
748 |
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758 |
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|
759 |
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" .colab-df-container {\n",
|
760 |
+
" display:flex;\n",
|
761 |
+
" flex-wrap:wrap;\n",
|
762 |
+
" gap: 12px;\n",
|
763 |
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" }\n",
|
764 |
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"\n",
|
765 |
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|
766 |
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|
767 |
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" border: none;\n",
|
768 |
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" border-radius: 50%;\n",
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774 |
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" width: 32px;\n",
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777 |
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779 |
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" fill: #174EA6;\n",
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" }\n",
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782 |
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"\n",
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783 |
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" [theme=dark] .colab-df-convert {\n",
|
784 |
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" background-color: #3B4455;\n",
|
785 |
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" fill: #D2E3FC;\n",
|
786 |
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" }\n",
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787 |
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"\n",
|
788 |
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" [theme=dark] .colab-df-convert:hover {\n",
|
789 |
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|
790 |
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" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
791 |
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" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
792 |
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" fill: #FFFFFF;\n",
|
793 |
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" }\n",
|
794 |
+
" </style>\n",
|
795 |
+
"\n",
|
796 |
+
" <script>\n",
|
797 |
+
" const buttonEl =\n",
|
798 |
+
" document.querySelector('#df-d62ae80e-064e-4795-9ce5-c8cc1659ce62 button.colab-df-convert');\n",
|
799 |
+
" buttonEl.style.display =\n",
|
800 |
+
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
801 |
+
"\n",
|
802 |
+
" async function convertToInteractive(key) {\n",
|
803 |
+
" const element = document.querySelector('#df-d62ae80e-064e-4795-9ce5-c8cc1659ce62');\n",
|
804 |
+
" const dataTable =\n",
|
805 |
+
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
806 |
+
" [key], {});\n",
|
807 |
+
" if (!dataTable) return;\n",
|
808 |
+
"\n",
|
809 |
+
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
810 |
+
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
811 |
+
" + ' to learn more about interactive tables.';\n",
|
812 |
+
" element.innerHTML = '';\n",
|
813 |
+
" dataTable['output_type'] = 'display_data';\n",
|
814 |
+
" await google.colab.output.renderOutput(dataTable, element);\n",
|
815 |
+
" const docLink = document.createElement('div');\n",
|
816 |
+
" docLink.innerHTML = docLinkHtml;\n",
|
817 |
+
" element.appendChild(docLink);\n",
|
818 |
+
" }\n",
|
819 |
+
" </script>\n",
|
820 |
+
" </div>\n",
|
821 |
+
" </div>\n",
|
822 |
+
" "
|
823 |
+
]
|
824 |
+
},
|
825 |
+
"metadata": {},
|
826 |
+
"execution_count": 6
|
827 |
+
}
|
828 |
+
],
|
829 |
+
"source": [
|
830 |
+
"df.loc[df.iloc[:, 2]==1].head()"
|
831 |
+
],
|
832 |
+
"id": "7eb94a81"
|
833 |
+
},
|
834 |
+
{
|
835 |
+
"cell_type": "code",
|
836 |
+
"execution_count": null,
|
837 |
+
"metadata": {
|
838 |
+
"colab": {
|
839 |
+
"base_uri": "https://localhost:8080/",
|
840 |
+
"height": 87
|
841 |
+
},
|
842 |
+
"id": "2bb35d57",
|
843 |
+
"outputId": "c9531968-a5f4-4348-a833-4a366ee59010"
|
844 |
+
},
|
845 |
+
"outputs": [
|
846 |
+
{
|
847 |
+
"output_type": "execute_result",
|
848 |
+
"data": {
|
849 |
+
"text/plain": [
|
850 |
+
"'Hey... what is it..\\n@ | talk .\\nWhat is it... an exclusive group of some WP TALIBANS...who are good at destroying, self-appointed purist who GANG UP any one who asks them questions abt their ANTI-SOCIAL and DESTRUCTIVE (non)-contribution at WP?\\n\\nAsk Sityush to clean up his behavior than issue me nonsensical warnings...'"
|
851 |
+
],
|
852 |
+
"application/vnd.google.colaboratory.intrinsic+json": {
|
853 |
+
"type": "string"
|
854 |
+
}
|
855 |
+
},
|
856 |
+
"metadata": {},
|
857 |
+
"execution_count": 7
|
858 |
+
}
|
859 |
+
],
|
860 |
+
"source": [
|
861 |
+
"df.iloc[12].comment_text"
|
862 |
+
],
|
863 |
+
"id": "2bb35d57"
|
864 |
+
},
|
865 |
+
{
|
866 |
+
"cell_type": "markdown",
|
867 |
+
"metadata": {
|
868 |
+
"id": "1fdd25c4"
|
869 |
+
},
|
870 |
+
"source": [
|
871 |
+
"## 2.2. Data preprocessing"
|
872 |
+
],
|
873 |
+
"id": "1fdd25c4"
|
874 |
+
},
|
875 |
+
{
|
876 |
+
"cell_type": "code",
|
877 |
+
"execution_count": null,
|
878 |
+
"metadata": {
|
879 |
+
"id": "c8bd9d59"
|
880 |
+
},
|
881 |
+
"outputs": [],
|
882 |
+
"source": [
|
883 |
+
"from tensorflow.keras.layers import TextVectorization"
|
884 |
+
],
|
885 |
+
"id": "c8bd9d59"
|
886 |
+
},
|
887 |
+
{
|
888 |
+
"cell_type": "code",
|
889 |
+
"execution_count": null,
|
890 |
+
"metadata": {
|
891 |
+
"colab": {
|
892 |
+
"base_uri": "https://localhost:8080/"
|
893 |
+
},
|
894 |
+
"id": "b8c03840",
|
895 |
+
"outputId": "d16ec2c2-b1d1-4956-a11b-6f8e1960a5b8"
|
896 |
+
},
|
897 |
+
"outputs": [
|
898 |
+
{
|
899 |
+
"output_type": "execute_result",
|
900 |
+
"data": {
|
901 |
+
"text/plain": [
|
902 |
+
"Index(['id', 'comment_text', 'toxic', 'severe_toxic', 'obscene', 'threat',\n",
|
903 |
+
" 'insult', 'identity_hate'],\n",
|
904 |
+
" dtype='object')"
|
905 |
+
]
|
906 |
+
},
|
907 |
+
"metadata": {},
|
908 |
+
"execution_count": 9
|
909 |
+
}
|
910 |
+
],
|
911 |
+
"source": [
|
912 |
+
"df.columns"
|
913 |
+
],
|
914 |
+
"id": "b8c03840"
|
915 |
+
},
|
916 |
+
{
|
917 |
+
"cell_type": "code",
|
918 |
+
"execution_count": null,
|
919 |
+
"metadata": {
|
920 |
+
"id": "2e64c456"
|
921 |
+
},
|
922 |
+
"outputs": [],
|
923 |
+
"source": [
|
924 |
+
"X = df.comment_text\n",
|
925 |
+
"y = df.iloc[:,2:].values"
|
926 |
+
],
|
927 |
+
"id": "2e64c456"
|
928 |
+
},
|
929 |
+
{
|
930 |
+
"cell_type": "code",
|
931 |
+
"execution_count": null,
|
932 |
+
"metadata": {
|
933 |
+
"id": "c924ed65"
|
934 |
+
},
|
935 |
+
"outputs": [],
|
936 |
+
"source": [
|
937 |
+
"# number of words in vocab\n",
|
938 |
+
"MAX_VOCAB = 200000"
|
939 |
+
],
|
940 |
+
"id": "c924ed65"
|
941 |
+
},
|
942 |
+
{
|
943 |
+
"cell_type": "code",
|
944 |
+
"execution_count": null,
|
945 |
+
"metadata": {
|
946 |
+
"id": "d9e74b26"
|
947 |
+
},
|
948 |
+
"outputs": [],
|
949 |
+
"source": [
|
950 |
+
"vectorizer = TextVectorization(max_tokens=MAX_VOCAB, \n",
|
951 |
+
" output_sequence_length=1800, \n",
|
952 |
+
" output_mode='int')"
|
953 |
+
],
|
954 |
+
"id": "d9e74b26"
|
955 |
+
},
|
956 |
+
{
|
957 |
+
"cell_type": "code",
|
958 |
+
"execution_count": null,
|
959 |
+
"metadata": {
|
960 |
+
"id": "b89a019a"
|
961 |
+
},
|
962 |
+
"outputs": [],
|
963 |
+
"source": [
|
964 |
+
"vectorizer.adapt(X.values)"
|
965 |
+
],
|
966 |
+
"id": "b89a019a"
|
967 |
+
},
|
968 |
+
{
|
969 |
+
"cell_type": "code",
|
970 |
+
"execution_count": null,
|
971 |
+
"metadata": {
|
972 |
+
"colab": {
|
973 |
+
"base_uri": "https://localhost:8080/"
|
974 |
+
},
|
975 |
+
"id": "832c78b5",
|
976 |
+
"outputId": "c5d8489f-1b6e-4bcc-e4e8-42359d85c4ce"
|
977 |
+
},
|
978 |
+
"outputs": [
|
979 |
+
{
|
980 |
+
"output_type": "execute_result",
|
981 |
+
"data": {
|
982 |
+
"text/plain": [
|
983 |
+
"<tf.Tensor: shape=(6,), dtype=int64, numpy=array([288, 263, 191, 3, 14, 463])>"
|
984 |
+
]
|
985 |
+
},
|
986 |
+
"metadata": {},
|
987 |
+
"execution_count": 14
|
988 |
+
}
|
989 |
+
],
|
990 |
+
"source": [
|
991 |
+
"vectorizer('Hello world, welcome to this project')[:6]"
|
992 |
+
],
|
993 |
+
"id": "832c78b5"
|
994 |
+
},
|
995 |
+
{
|
996 |
+
"cell_type": "code",
|
997 |
+
"execution_count": null,
|
998 |
+
"metadata": {
|
999 |
+
"id": "d90fea8a"
|
1000 |
+
},
|
1001 |
+
"outputs": [],
|
1002 |
+
"source": [
|
1003 |
+
"processed_text = vectorizer(X.values)"
|
1004 |
+
],
|
1005 |
+
"id": "d90fea8a"
|
1006 |
+
},
|
1007 |
+
{
|
1008 |
+
"cell_type": "code",
|
1009 |
+
"execution_count": null,
|
1010 |
+
"metadata": {
|
1011 |
+
"colab": {
|
1012 |
+
"base_uri": "https://localhost:8080/"
|
1013 |
+
},
|
1014 |
+
"id": "9891f1b3",
|
1015 |
+
"outputId": "16715f82-cc03-4bc9-bc4c-0d964111d0d3"
|
1016 |
+
},
|
1017 |
+
"outputs": [
|
1018 |
+
{
|
1019 |
+
"output_type": "execute_result",
|
1020 |
+
"data": {
|
1021 |
+
"text/plain": [
|
1022 |
+
"<tf.Tensor: shape=(159571, 1800), dtype=int64, numpy=\n",
|
1023 |
+
"array([[ 645, 76, 2, ..., 0, 0, 0],\n",
|
1024 |
+
" [ 1, 54, 2489, ..., 0, 0, 0],\n",
|
1025 |
+
" [ 425, 441, 70, ..., 0, 0, 0],\n",
|
1026 |
+
" ...,\n",
|
1027 |
+
" [32445, 7392, 383, ..., 0, 0, 0],\n",
|
1028 |
+
" [ 5, 12, 534, ..., 0, 0, 0],\n",
|
1029 |
+
" [ 5, 8, 130, ..., 0, 0, 0]])>"
|
1030 |
+
]
|
1031 |
+
},
|
1032 |
+
"metadata": {},
|
1033 |
+
"execution_count": 16
|
1034 |
+
}
|
1035 |
+
],
|
1036 |
+
"source": [
|
1037 |
+
"processed_text"
|
1038 |
+
],
|
1039 |
+
"id": "9891f1b3"
|
1040 |
+
},
|
1041 |
+
{
|
1042 |
+
"cell_type": "code",
|
1043 |
+
"execution_count": null,
|
1044 |
+
"metadata": {
|
1045 |
+
"id": "9176a3c0"
|
1046 |
+
},
|
1047 |
+
"outputs": [],
|
1048 |
+
"source": [
|
1049 |
+
"# MCSHBAP - map, cache, shuffle, batch, prefetch\n",
|
1050 |
+
"# from_tensor_slices OR list_file\n",
|
1051 |
+
"data = tf.data.Dataset.from_tensor_slices((processed_text, y))\n",
|
1052 |
+
"data = data.cache()\n",
|
1053 |
+
"data = data.shuffle(160000)\n",
|
1054 |
+
"data = data.batch(16)\n",
|
1055 |
+
"data = data.prefetch(8) # prevent bottleneck"
|
1056 |
+
],
|
1057 |
+
"id": "9176a3c0"
|
1058 |
+
},
|
1059 |
+
{
|
1060 |
+
"cell_type": "code",
|
1061 |
+
"execution_count": null,
|
1062 |
+
"metadata": {
|
1063 |
+
"id": "042126d5"
|
1064 |
+
},
|
1065 |
+
"outputs": [],
|
1066 |
+
"source": [
|
1067 |
+
"batch_X, batch_y = data.as_numpy_iterator().next()"
|
1068 |
+
],
|
1069 |
+
"id": "042126d5"
|
1070 |
+
},
|
1071 |
+
{
|
1072 |
+
"cell_type": "code",
|
1073 |
+
"execution_count": null,
|
1074 |
+
"metadata": {
|
1075 |
+
"colab": {
|
1076 |
+
"base_uri": "https://localhost:8080/"
|
1077 |
+
},
|
1078 |
+
"id": "5b73aea2",
|
1079 |
+
"outputId": "be6a586c-2d6e-459a-8748-ae4b4ec03125"
|
1080 |
+
},
|
1081 |
+
"outputs": [
|
1082 |
+
{
|
1083 |
+
"output_type": "execute_result",
|
1084 |
+
"data": {
|
1085 |
+
"text/plain": [
|
1086 |
+
"(16, 1800)"
|
1087 |
+
]
|
1088 |
+
},
|
1089 |
+
"metadata": {},
|
1090 |
+
"execution_count": 19
|
1091 |
+
}
|
1092 |
+
],
|
1093 |
+
"source": [
|
1094 |
+
"batch_X.shape"
|
1095 |
+
],
|
1096 |
+
"id": "5b73aea2"
|
1097 |
+
},
|
1098 |
+
{
|
1099 |
+
"cell_type": "code",
|
1100 |
+
"execution_count": null,
|
1101 |
+
"metadata": {
|
1102 |
+
"id": "8286ce71"
|
1103 |
+
},
|
1104 |
+
"outputs": [],
|
1105 |
+
"source": [
|
1106 |
+
"train = data.take(int(len(data) * .7))\n",
|
1107 |
+
"val = data.skip(int(len(data) * .7)).take(int(len(data)*.2))\n",
|
1108 |
+
"test = data.take(int(len(data) * .9)).take(int(len(data)*.1))"
|
1109 |
+
],
|
1110 |
+
"id": "8286ce71"
|
1111 |
+
},
|
1112 |
+
{
|
1113 |
+
"cell_type": "code",
|
1114 |
+
"execution_count": null,
|
1115 |
+
"metadata": {
|
1116 |
+
"colab": {
|
1117 |
+
"base_uri": "https://localhost:8080/"
|
1118 |
+
},
|
1119 |
+
"id": "f06e8067",
|
1120 |
+
"outputId": "8dadd560-5bfb-4d58-8301-d9f56f30a0b0"
|
1121 |
+
},
|
1122 |
+
"outputs": [
|
1123 |
+
{
|
1124 |
+
"output_type": "execute_result",
|
1125 |
+
"data": {
|
1126 |
+
"text/plain": [
|
1127 |
+
"6981"
|
1128 |
+
]
|
1129 |
+
},
|
1130 |
+
"metadata": {},
|
1131 |
+
"execution_count": 21
|
1132 |
+
}
|
1133 |
+
],
|
1134 |
+
"source": [
|
1135 |
+
"len(train)"
|
1136 |
+
],
|
1137 |
+
"id": "f06e8067"
|
1138 |
+
},
|
1139 |
+
{
|
1140 |
+
"cell_type": "code",
|
1141 |
+
"execution_count": null,
|
1142 |
+
"metadata": {
|
1143 |
+
"colab": {
|
1144 |
+
"base_uri": "https://localhost:8080/"
|
1145 |
+
},
|
1146 |
+
"id": "74d5fb4e",
|
1147 |
+
"outputId": "7ddc0d55-360e-4283-a955-ce3dab49fc07"
|
1148 |
+
},
|
1149 |
+
"outputs": [
|
1150 |
+
{
|
1151 |
+
"output_type": "execute_result",
|
1152 |
+
"data": {
|
1153 |
+
"text/plain": [
|
1154 |
+
"(array([[ 5495, 51, 29, ..., 0, 0, 0],\n",
|
1155 |
+
" [ 33, 7, 69, ..., 0, 0, 0],\n",
|
1156 |
+
" [ 24, 1805, 2256, ..., 0, 0, 0],\n",
|
1157 |
+
" ...,\n",
|
1158 |
+
" [ 46, 1377, 31, ..., 0, 0, 0],\n",
|
1159 |
+
" [ 4354, 41514, 8, ..., 0, 0, 0],\n",
|
1160 |
+
" [ 215, 8, 477, ..., 0, 0, 0]]),\n",
|
1161 |
+
" array([[0, 0, 0, 0, 0, 0],\n",
|
1162 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1163 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1164 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1165 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1166 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1167 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1168 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1169 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1170 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1171 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1172 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1173 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1174 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1175 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1176 |
+
" [0, 0, 0, 0, 0, 0]]))"
|
1177 |
+
]
|
1178 |
+
},
|
1179 |
+
"metadata": {},
|
1180 |
+
"execution_count": 22
|
1181 |
+
}
|
1182 |
+
],
|
1183 |
+
"source": [
|
1184 |
+
"train.as_numpy_iterator().next()"
|
1185 |
+
],
|
1186 |
+
"id": "74d5fb4e"
|
1187 |
+
},
|
1188 |
+
{
|
1189 |
+
"cell_type": "markdown",
|
1190 |
+
"metadata": {
|
1191 |
+
"id": "-8f_Bi-OAc03"
|
1192 |
+
},
|
1193 |
+
"source": [
|
1194 |
+
"# 3. Buiding model"
|
1195 |
+
],
|
1196 |
+
"id": "-8f_Bi-OAc03"
|
1197 |
+
},
|
1198 |
+
{
|
1199 |
+
"cell_type": "code",
|
1200 |
+
"execution_count": null,
|
1201 |
+
"metadata": {
|
1202 |
+
"id": "ItiVy4-S1pK5"
|
1203 |
+
},
|
1204 |
+
"outputs": [],
|
1205 |
+
"source": [
|
1206 |
+
"from tensorflow.keras.models import Sequential\n",
|
1207 |
+
"from tensorflow.keras.layers import LSTM, Dropout, Bidirectional, Dense, Embedding"
|
1208 |
+
],
|
1209 |
+
"id": "ItiVy4-S1pK5"
|
1210 |
+
},
|
1211 |
+
{
|
1212 |
+
"cell_type": "code",
|
1213 |
+
"execution_count": null,
|
1214 |
+
"metadata": {
|
1215 |
+
"id": "8U9TmSbxAvEw"
|
1216 |
+
},
|
1217 |
+
"outputs": [],
|
1218 |
+
"source": [
|
1219 |
+
"model = Sequential()\n",
|
1220 |
+
"model.add(Embedding(MAX_VOCAB + 1, 32))\n",
|
1221 |
+
"model.add(Bidirectional(LSTM(32, activation='tanh')))\n",
|
1222 |
+
"model.add(Dense(128, activation='relu'))\n",
|
1223 |
+
"model.add(Dense(256, activation='relu'))\n",
|
1224 |
+
"model.add(Dense(128, activation='relu'))\n",
|
1225 |
+
"model.add(Dense(64, activation='relu'))\n",
|
1226 |
+
"model.add(Dense(32, activation='relu'))\n",
|
1227 |
+
"model.add(Dense(6, activation='sigmoid'))"
|
1228 |
+
],
|
1229 |
+
"id": "8U9TmSbxAvEw"
|
1230 |
+
},
|
1231 |
+
{
|
1232 |
+
"cell_type": "code",
|
1233 |
+
"execution_count": null,
|
1234 |
+
"metadata": {
|
1235 |
+
"id": "pF_pooL4CY91"
|
1236 |
+
},
|
1237 |
+
"outputs": [],
|
1238 |
+
"source": [
|
1239 |
+
"model.compile(loss='BinaryCrossentropy', optimizer='Adam')"
|
1240 |
+
],
|
1241 |
+
"id": "pF_pooL4CY91"
|
1242 |
+
},
|
1243 |
+
{
|
1244 |
+
"cell_type": "code",
|
1245 |
+
"execution_count": null,
|
1246 |
+
"metadata": {
|
1247 |
+
"colab": {
|
1248 |
+
"base_uri": "https://localhost:8080/"
|
1249 |
+
},
|
1250 |
+
"id": "ZtPm1Gp2GJza",
|
1251 |
+
"outputId": "222a35f6-4ad4-4c16-a240-3a18c0392525"
|
1252 |
+
},
|
1253 |
+
"outputs": [
|
1254 |
+
{
|
1255 |
+
"output_type": "stream",
|
1256 |
+
"name": "stdout",
|
1257 |
+
"text": [
|
1258 |
+
"Model: \"sequential_2\"\n",
|
1259 |
+
"_________________________________________________________________\n",
|
1260 |
+
" Layer (type) Output Shape Param # \n",
|
1261 |
+
"=================================================================\n",
|
1262 |
+
" embedding_2 (Embedding) (None, None, 32) 6400032 \n",
|
1263 |
+
" \n",
|
1264 |
+
" bidirectional_2 (Bidirectio (None, 64) 16640 \n",
|
1265 |
+
" nal) \n",
|
1266 |
+
" \n",
|
1267 |
+
" dense_12 (Dense) (None, 128) 8320 \n",
|
1268 |
+
" \n",
|
1269 |
+
" dense_13 (Dense) (None, 256) 33024 \n",
|
1270 |
+
" \n",
|
1271 |
+
" dense_14 (Dense) (None, 128) 32896 \n",
|
1272 |
+
" \n",
|
1273 |
+
" dense_15 (Dense) (None, 64) 8256 \n",
|
1274 |
+
" \n",
|
1275 |
+
" dense_16 (Dense) (None, 32) 2080 \n",
|
1276 |
+
" \n",
|
1277 |
+
" dense_17 (Dense) (None, 6) 198 \n",
|
1278 |
+
" \n",
|
1279 |
+
"=================================================================\n",
|
1280 |
+
"Total params: 6,501,446\n",
|
1281 |
+
"Trainable params: 6,501,446\n",
|
1282 |
+
"Non-trainable params: 0\n",
|
1283 |
+
"_________________________________________________________________\n"
|
1284 |
+
]
|
1285 |
+
}
|
1286 |
+
],
|
1287 |
+
"source": [
|
1288 |
+
"model.summary()"
|
1289 |
+
],
|
1290 |
+
"id": "ZtPm1Gp2GJza"
|
1291 |
+
},
|
1292 |
+
{
|
1293 |
+
"cell_type": "code",
|
1294 |
+
"execution_count": null,
|
1295 |
+
"metadata": {
|
1296 |
+
"colab": {
|
1297 |
+
"base_uri": "https://localhost:8080/"
|
1298 |
+
},
|
1299 |
+
"id": "Cu-uCQaEJpjK",
|
1300 |
+
"outputId": "dd00becf-d085-47d2-ad04-fc121471ebef"
|
1301 |
+
},
|
1302 |
+
"outputs": [
|
1303 |
+
{
|
1304 |
+
"output_type": "stream",
|
1305 |
+
"name": "stdout",
|
1306 |
+
"text": [
|
1307 |
+
"Epoch 1/10\n",
|
1308 |
+
"6981/6981 [==============================] - 642s 92ms/step - loss: 0.0645 - val_loss: 0.0441\n",
|
1309 |
+
"Epoch 2/10\n",
|
1310 |
+
"6981/6981 [==============================] - 639s 91ms/step - loss: 0.0458 - val_loss: 0.0398\n",
|
1311 |
+
"Epoch 3/10\n",
|
1312 |
+
"6981/6981 [==============================] - 660s 94ms/step - loss: 0.0412 - val_loss: 0.0366\n",
|
1313 |
+
"Epoch 4/10\n",
|
1314 |
+
"6981/6981 [==============================] - 639s 91ms/step - loss: 0.0371 - val_loss: 0.0335\n",
|
1315 |
+
"Epoch 5/10\n",
|
1316 |
+
"6981/6981 [==============================] - 648s 93ms/step - loss: 0.0335 - val_loss: 0.0297\n",
|
1317 |
+
"Epoch 6/10\n",
|
1318 |
+
"6981/6981 [==============================] - 634s 91ms/step - loss: 0.0307 - val_loss: 0.0261\n",
|
1319 |
+
"Epoch 7/10\n",
|
1320 |
+
"6981/6981 [==============================] - 634s 91ms/step - loss: 0.0278 - val_loss: 0.0254\n",
|
1321 |
+
"Epoch 8/10\n",
|
1322 |
+
"6981/6981 [==============================] - 634s 91ms/step - loss: 0.0252 - val_loss: 0.0231\n",
|
1323 |
+
"Epoch 9/10\n",
|
1324 |
+
"6981/6981 [==============================] - 623s 89ms/step - loss: 0.0234 - val_loss: 0.0193\n",
|
1325 |
+
"Epoch 10/10\n",
|
1326 |
+
"6981/6981 [==============================] - 627s 90ms/step - loss: 0.0214 - val_loss: 0.0197\n"
|
1327 |
+
]
|
1328 |
+
}
|
1329 |
+
],
|
1330 |
+
"source": [
|
1331 |
+
"history = model.fit(train, epochs=10, validation_data=val)"
|
1332 |
+
],
|
1333 |
+
"id": "Cu-uCQaEJpjK"
|
1334 |
+
},
|
1335 |
+
{
|
1336 |
+
"cell_type": "code",
|
1337 |
+
"execution_count": null,
|
1338 |
+
"metadata": {
|
1339 |
+
"id": "Ylqg0nwFGPBL"
|
1340 |
+
},
|
1341 |
+
"outputs": [],
|
1342 |
+
"source": [
|
1343 |
+
"import matplotlib.pyplot as plt"
|
1344 |
+
],
|
1345 |
+
"id": "Ylqg0nwFGPBL"
|
1346 |
+
},
|
1347 |
+
{
|
1348 |
+
"cell_type": "code",
|
1349 |
+
"execution_count": null,
|
1350 |
+
"metadata": {
|
1351 |
+
"id": "cD_u8JR4OYFL",
|
1352 |
+
"colab": {
|
1353 |
+
"base_uri": "https://localhost:8080/",
|
1354 |
+
"height": 282
|
1355 |
+
},
|
1356 |
+
"outputId": "61bab891-c2a2-44f4-afa4-c17e7aa37f05"
|
1357 |
+
},
|
1358 |
+
"outputs": [
|
1359 |
+
{
|
1360 |
+
"output_type": "display_data",
|
1361 |
+
"data": {
|
1362 |
+
"text/plain": [
|
1363 |
+
"<Figure size 576x360 with 0 Axes>"
|
1364 |
+
]
|
1365 |
+
},
|
1366 |
+
"metadata": {}
|
1367 |
+
},
|
1368 |
+
{
|
1369 |
+
"output_type": "display_data",
|
1370 |
+
"data": {
|
1371 |
+
"text/plain": [
|
1372 |
+
"<Figure size 432x288 with 1 Axes>"
|
1373 |
+
],
|
1374 |
+
"image/png": 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\n"
|
1375 |
+
},
|
1376 |
+
"metadata": {
|
1377 |
+
"needs_background": "light"
|
1378 |
+
}
|
1379 |
+
}
|
1380 |
+
],
|
1381 |
+
"source": [
|
1382 |
+
"plt.figure(figsize=(8, 5))\n",
|
1383 |
+
"pd.DataFrame(history.history).plot()\n",
|
1384 |
+
"plt.show()"
|
1385 |
+
],
|
1386 |
+
"id": "cD_u8JR4OYFL"
|
1387 |
+
},
|
1388 |
+
{
|
1389 |
+
"cell_type": "markdown",
|
1390 |
+
"metadata": {
|
1391 |
+
"id": "OJxNheOEVGoD"
|
1392 |
+
},
|
1393 |
+
"source": [
|
1394 |
+
"# 4. Make predictions"
|
1395 |
+
],
|
1396 |
+
"id": "OJxNheOEVGoD"
|
1397 |
+
},
|
1398 |
+
{
|
1399 |
+
"cell_type": "code",
|
1400 |
+
"execution_count": null,
|
1401 |
+
"metadata": {
|
1402 |
+
"id": "qAlM31wVVFIx",
|
1403 |
+
"colab": {
|
1404 |
+
"base_uri": "https://localhost:8080/"
|
1405 |
+
},
|
1406 |
+
"outputId": "86d60e93-348e-478b-991c-d5e86693157a"
|
1407 |
+
},
|
1408 |
+
"outputs": [
|
1409 |
+
{
|
1410 |
+
"output_type": "execute_result",
|
1411 |
+
"data": {
|
1412 |
+
"text/plain": [
|
1413 |
+
"<tf.Tensor: shape=(1800,), dtype=int64, numpy=array([ 7, 318, 0, ..., 0, 0, 0])>"
|
1414 |
+
]
|
1415 |
+
},
|
1416 |
+
"metadata": {},
|
1417 |
+
"execution_count": 64
|
1418 |
+
}
|
1419 |
+
],
|
1420 |
+
"source": [
|
1421 |
+
"text = vectorizer(\"you shit\")\n",
|
1422 |
+
"text"
|
1423 |
+
],
|
1424 |
+
"id": "qAlM31wVVFIx"
|
1425 |
+
},
|
1426 |
+
{
|
1427 |
+
"cell_type": "code",
|
1428 |
+
"execution_count": null,
|
1429 |
+
"metadata": {
|
1430 |
+
"id": "5Nlk_v_Da-Pi",
|
1431 |
+
"colab": {
|
1432 |
+
"base_uri": "https://localhost:8080/"
|
1433 |
+
},
|
1434 |
+
"outputId": "ad328b76-840f-44e9-d048-23d6d5443cd9"
|
1435 |
+
},
|
1436 |
+
"outputs": [
|
1437 |
+
{
|
1438 |
+
"output_type": "execute_result",
|
1439 |
+
"data": {
|
1440 |
+
"text/plain": [
|
1441 |
+
"array([[ 7, 318, 0, ..., 0, 0, 0]])"
|
1442 |
+
]
|
1443 |
+
},
|
1444 |
+
"metadata": {},
|
1445 |
+
"execution_count": 65
|
1446 |
+
}
|
1447 |
+
],
|
1448 |
+
"source": [
|
1449 |
+
"np.expand_dims(text, 0)"
|
1450 |
+
],
|
1451 |
+
"id": "5Nlk_v_Da-Pi"
|
1452 |
+
},
|
1453 |
+
{
|
1454 |
+
"cell_type": "code",
|
1455 |
+
"execution_count": null,
|
1456 |
+
"metadata": {
|
1457 |
+
"id": "ReideBKOVhAY",
|
1458 |
+
"colab": {
|
1459 |
+
"base_uri": "https://localhost:8080/"
|
1460 |
+
},
|
1461 |
+
"outputId": "5e6e9aab-332b-4de0-a590-f55d2dc6bfdf"
|
1462 |
+
},
|
1463 |
+
"outputs": [
|
1464 |
+
{
|
1465 |
+
"output_type": "execute_result",
|
1466 |
+
"data": {
|
1467 |
+
"text/plain": [
|
1468 |
+
"array([[0.9876286 , 0.15251058, 0.9701179 , 0.0023339 , 0.33286613,\n",
|
1469 |
+
" 0.00344882]], dtype=float32)"
|
1470 |
+
]
|
1471 |
+
},
|
1472 |
+
"metadata": {},
|
1473 |
+
"execution_count": 66
|
1474 |
+
}
|
1475 |
+
],
|
1476 |
+
"source": [
|
1477 |
+
"res = model.predict(np.expand_dims(text, 0))\n",
|
1478 |
+
"res"
|
1479 |
+
],
|
1480 |
+
"id": "ReideBKOVhAY"
|
1481 |
+
},
|
1482 |
+
{
|
1483 |
+
"cell_type": "code",
|
1484 |
+
"execution_count": null,
|
1485 |
+
"metadata": {
|
1486 |
+
"id": "-uAI_l6XVvMC",
|
1487 |
+
"colab": {
|
1488 |
+
"base_uri": "https://localhost:8080/"
|
1489 |
+
},
|
1490 |
+
"outputId": "76562f4d-0884-4e9b-96e9-351bc933a66e"
|
1491 |
+
},
|
1492 |
+
"outputs": [
|
1493 |
+
{
|
1494 |
+
"output_type": "execute_result",
|
1495 |
+
"data": {
|
1496 |
+
"text/plain": [
|
1497 |
+
"Index(['toxic', 'severe_toxic', 'obscene', 'threat', 'insult',\n",
|
1498 |
+
" 'identity_hate'],\n",
|
1499 |
+
" dtype='object')"
|
1500 |
+
]
|
1501 |
+
},
|
1502 |
+
"metadata": {},
|
1503 |
+
"execution_count": 67
|
1504 |
+
}
|
1505 |
+
],
|
1506 |
+
"source": [
|
1507 |
+
"df.columns[2:]"
|
1508 |
+
],
|
1509 |
+
"id": "-uAI_l6XVvMC"
|
1510 |
+
},
|
1511 |
+
{
|
1512 |
+
"cell_type": "code",
|
1513 |
+
"execution_count": null,
|
1514 |
+
"metadata": {
|
1515 |
+
"id": "ROi-r6MGVT1T"
|
1516 |
+
},
|
1517 |
+
"outputs": [],
|
1518 |
+
"source": [
|
1519 |
+
"batch_X, batch_y = test.as_numpy_iterator().next()"
|
1520 |
+
],
|
1521 |
+
"id": "ROi-r6MGVT1T"
|
1522 |
+
},
|
1523 |
+
{
|
1524 |
+
"cell_type": "code",
|
1525 |
+
"execution_count": null,
|
1526 |
+
"metadata": {
|
1527 |
+
"id": "vcTgLwQjYehR",
|
1528 |
+
"colab": {
|
1529 |
+
"base_uri": "https://localhost:8080/"
|
1530 |
+
},
|
1531 |
+
"outputId": "8b25d0d8-bbe4-49ac-e67f-e8a929524bae"
|
1532 |
+
},
|
1533 |
+
"outputs": [
|
1534 |
+
{
|
1535 |
+
"output_type": "execute_result",
|
1536 |
+
"data": {
|
1537 |
+
"text/plain": [
|
1538 |
+
"array([[0, 0, 0, 0, 0, 0],\n",
|
1539 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1540 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1541 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1542 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1543 |
+
" [1, 0, 1, 0, 1, 0],\n",
|
1544 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1545 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1546 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1547 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1548 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1549 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1550 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1551 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1552 |
+
" [0, 0, 0, 0, 0, 0],\n",
|
1553 |
+
" [0, 0, 0, 0, 0, 0]])"
|
1554 |
+
]
|
1555 |
+
},
|
1556 |
+
"metadata": {},
|
1557 |
+
"execution_count": 69
|
1558 |
+
}
|
1559 |
+
],
|
1560 |
+
"source": [
|
1561 |
+
"pred = (model.predict(batch_X) > 0.5).astype(int)\n",
|
1562 |
+
"pred"
|
1563 |
+
],
|
1564 |
+
"id": "vcTgLwQjYehR"
|
1565 |
+
},
|
1566 |
+
{
|
1567 |
+
"cell_type": "code",
|
1568 |
+
"execution_count": null,
|
1569 |
+
"metadata": {
|
1570 |
+
"id": "kVWGgNWxc1LY",
|
1571 |
+
"colab": {
|
1572 |
+
"base_uri": "https://localhost:8080/"
|
1573 |
+
},
|
1574 |
+
"outputId": "8be7ac60-e9d2-4007-9c06-358f1a58ab89"
|
1575 |
+
},
|
1576 |
+
"outputs": [
|
1577 |
+
{
|
1578 |
+
"output_type": "execute_result",
|
1579 |
+
"data": {
|
1580 |
+
"text/plain": [
|
1581 |
+
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
1582 |
+
" 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
1583 |
+
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
1584 |
+
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
|
1585 |
+
" 0, 0, 0, 0, 0, 0, 0, 0])"
|
1586 |
+
]
|
1587 |
+
},
|
1588 |
+
"metadata": {},
|
1589 |
+
"execution_count": 70
|
1590 |
+
}
|
1591 |
+
],
|
1592 |
+
"source": [
|
1593 |
+
"pred = pred.flatten()\n",
|
1594 |
+
"pred"
|
1595 |
+
],
|
1596 |
+
"id": "kVWGgNWxc1LY"
|
1597 |
+
},
|
1598 |
+
{
|
1599 |
+
"cell_type": "markdown",
|
1600 |
+
"metadata": {
|
1601 |
+
"id": "INW-U2pcaXHV"
|
1602 |
+
},
|
1603 |
+
"source": [
|
1604 |
+
"# 5. Evaluate model"
|
1605 |
+
],
|
1606 |
+
"id": "INW-U2pcaXHV"
|
1607 |
+
},
|
1608 |
+
{
|
1609 |
+
"cell_type": "code",
|
1610 |
+
"execution_count": null,
|
1611 |
+
"metadata": {
|
1612 |
+
"id": "6UfuO4WBaWre"
|
1613 |
+
},
|
1614 |
+
"outputs": [],
|
1615 |
+
"source": [
|
1616 |
+
"from tensorflow.keras.metrics import Precision, Recall, CategoricalAccuracy"
|
1617 |
+
],
|
1618 |
+
"id": "6UfuO4WBaWre"
|
1619 |
+
},
|
1620 |
+
{
|
1621 |
+
"cell_type": "code",
|
1622 |
+
"execution_count": null,
|
1623 |
+
"metadata": {
|
1624 |
+
"id": "zJ-1rJDuaJCp"
|
1625 |
+
},
|
1626 |
+
"outputs": [],
|
1627 |
+
"source": [
|
1628 |
+
"pre = Precision()\n",
|
1629 |
+
"re = Recall()\n",
|
1630 |
+
"acc = CategoricalAccuracy()"
|
1631 |
+
],
|
1632 |
+
"id": "zJ-1rJDuaJCp"
|
1633 |
+
},
|
1634 |
+
{
|
1635 |
+
"cell_type": "code",
|
1636 |
+
"execution_count": null,
|
1637 |
+
"metadata": {
|
1638 |
+
"id": "sQFmLI5JbQJZ"
|
1639 |
+
},
|
1640 |
+
"outputs": [],
|
1641 |
+
"source": [
|
1642 |
+
"for batch in test.as_numpy_iterator():\n",
|
1643 |
+
" X_true, y_true = batch\n",
|
1644 |
+
" pred = model.predict(X_true)\n",
|
1645 |
+
"\n",
|
1646 |
+
" y_true = y_true.flatten()\n",
|
1647 |
+
" pred = pred.flatten()\n",
|
1648 |
+
"\n",
|
1649 |
+
" pre.update_state(y_true, pred)\n",
|
1650 |
+
" re.update_state(y_true, pred)\n",
|
1651 |
+
" acc.update_state(y_true, pred)"
|
1652 |
+
],
|
1653 |
+
"id": "sQFmLI5JbQJZ"
|
1654 |
+
},
|
1655 |
+
{
|
1656 |
+
"cell_type": "code",
|
1657 |
+
"execution_count": null,
|
1658 |
+
"metadata": {
|
1659 |
+
"id": "TRs7GXOddNAw",
|
1660 |
+
"colab": {
|
1661 |
+
"base_uri": "https://localhost:8080/"
|
1662 |
+
},
|
1663 |
+
"outputId": "95910681-d680-4272-94bd-c6a94b4bfcc0"
|
1664 |
+
},
|
1665 |
+
"outputs": [
|
1666 |
+
{
|
1667 |
+
"output_type": "stream",
|
1668 |
+
"name": "stdout",
|
1669 |
+
"text": [
|
1670 |
+
"Precision: 0.9102380275726318, Recall: 0.9139072895050049, Accuracy: 0.49949848651885986\n"
|
1671 |
+
]
|
1672 |
+
}
|
1673 |
+
],
|
1674 |
+
"source": [
|
1675 |
+
"print(f\"Precision: {pre.result().numpy()}, Recall: {re.result().numpy()}, Accuracy: {acc.result().numpy()}\")"
|
1676 |
+
],
|
1677 |
+
"id": "TRs7GXOddNAw"
|
1678 |
+
},
|
1679 |
+
{
|
1680 |
+
"cell_type": "code",
|
1681 |
+
"execution_count": null,
|
1682 |
+
"metadata": {
|
1683 |
+
"id": "1oEUJDL5eymH"
|
1684 |
+
},
|
1685 |
+
"outputs": [],
|
1686 |
+
"source": [
|
1687 |
+
"model.save('toxic-detect.h5')"
|
1688 |
+
],
|
1689 |
+
"id": "1oEUJDL5eymH"
|
1690 |
+
},
|
1691 |
+
{
|
1692 |
+
"cell_type": "markdown",
|
1693 |
+
"metadata": {
|
1694 |
+
"id": "jFglatzteIXT"
|
1695 |
+
},
|
1696 |
+
"source": [
|
1697 |
+
"# 5. Test and Gradio"
|
1698 |
+
],
|
1699 |
+
"id": "jFglatzteIXT"
|
1700 |
+
},
|
1701 |
+
{
|
1702 |
+
"cell_type": "code",
|
1703 |
+
"execution_count": null,
|
1704 |
+
"metadata": {
|
1705 |
+
"id": "Tg_jFNCOdC3V"
|
1706 |
+
},
|
1707 |
+
"outputs": [],
|
1708 |
+
"source": [
|
1709 |
+
"!pip install gradio jinja2"
|
1710 |
+
],
|
1711 |
+
"id": "Tg_jFNCOdC3V"
|
1712 |
+
},
|
1713 |
+
{
|
1714 |
+
"cell_type": "code",
|
1715 |
+
"execution_count": null,
|
1716 |
+
"metadata": {
|
1717 |
+
"id": "dKH2Er6Eenim"
|
1718 |
+
},
|
1719 |
+
"outputs": [],
|
1720 |
+
"source": [
|
1721 |
+
"import gradio as gr"
|
1722 |
+
],
|
1723 |
+
"id": "dKH2Er6Eenim"
|
1724 |
+
},
|
1725 |
+
{
|
1726 |
+
"cell_type": "code",
|
1727 |
+
"execution_count": null,
|
1728 |
+
"metadata": {
|
1729 |
+
"id": "JES3zWnRfHKt"
|
1730 |
+
},
|
1731 |
+
"outputs": [],
|
1732 |
+
"source": [
|
1733 |
+
"model = tf.keras.models.load_model('toxic-detect.h5')"
|
1734 |
+
],
|
1735 |
+
"id": "JES3zWnRfHKt"
|
1736 |
+
},
|
1737 |
+
{
|
1738 |
+
"cell_type": "code",
|
1739 |
+
"execution_count": null,
|
1740 |
+
"metadata": {
|
1741 |
+
"id": "q_zuX1vVfYHq"
|
1742 |
+
},
|
1743 |
+
"outputs": [],
|
1744 |
+
"source": [
|
1745 |
+
"def evaluate_comment(Comment):\n",
|
1746 |
+
" processed_Comment = vectorizer([Comment])\n",
|
1747 |
+
" res = model.predict(processed_Comment)\n",
|
1748 |
+
"\n",
|
1749 |
+
" text = ''\n",
|
1750 |
+
" for i, col in enumerate(df.columns[2:]):\n",
|
1751 |
+
" text += '{}: {}\\n'.format(col, 'Violate' if res[0][i] > 0.5 else 'None')\n",
|
1752 |
+
" \n",
|
1753 |
+
" return text"
|
1754 |
+
],
|
1755 |
+
"id": "q_zuX1vVfYHq"
|
1756 |
+
},
|
1757 |
+
{
|
1758 |
+
"cell_type": "code",
|
1759 |
+
"execution_count": null,
|
1760 |
+
"metadata": {
|
1761 |
+
"id": "TpJeqs__gsCh"
|
1762 |
+
},
|
1763 |
+
"outputs": [],
|
1764 |
+
"source": [
|
1765 |
+
"interface = gr.Interface(fn = evaluate_comment, \n",
|
1766 |
+
" inputs = gr.inputs.Textbox(lines = 4, placeholder='Comment to evaluate'), \n",
|
1767 |
+
" outputs = 'text')"
|
1768 |
+
],
|
1769 |
+
"id": "TpJeqs__gsCh"
|
1770 |
+
},
|
1771 |
+
{
|
1772 |
+
"cell_type": "code",
|
1773 |
+
"execution_count": null,
|
1774 |
+
"metadata": {
|
1775 |
+
"id": "a3DOdPazhGuW"
|
1776 |
+
},
|
1777 |
+
"outputs": [],
|
1778 |
+
"source": [
|
1779 |
+
"interface.launch(share=True)"
|
1780 |
+
],
|
1781 |
+
"id": "a3DOdPazhGuW"
|
1782 |
+
}
|
1783 |
+
],
|
1784 |
+
"metadata": {
|
1785 |
+
"accelerator": "GPU",
|
1786 |
+
"colab": {
|
1787 |
+
"collapsed_sections": [],
|
1788 |
+
"provenance": []
|
1789 |
+
},
|
1790 |
+
"kernelspec": {
|
1791 |
+
"display_name": "Python 3 (ipykernel)",
|
1792 |
+
"language": "python",
|
1793 |
+
"name": "python3"
|
1794 |
+
},
|
1795 |
+
"language_info": {
|
1796 |
+
"codemirror_mode": {
|
1797 |
+
"name": "ipython",
|
1798 |
+
"version": 3
|
1799 |
+
},
|
1800 |
+
"file_extension": ".py",
|
1801 |
+
"mimetype": "text/x-python",
|
1802 |
+
"name": "python",
|
1803 |
+
"nbconvert_exporter": "python",
|
1804 |
+
"pygments_lexer": "ipython3",
|
1805 |
+
"version": "3.10.6"
|
1806 |
+
}
|
1807 |
+
},
|
1808 |
+
"nbformat": 4,
|
1809 |
+
"nbformat_minor": 5
|
1810 |
+
}
|