forked from fastai/fastai
-
Notifications
You must be signed in to change notification settings - Fork 0
/
text.html
758 lines (618 loc) · 27.3 KB
/
text.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
---
title: text
keywords: fastai
sidebar: home_sidebar
summary: "Application to NLP, including ULMFiT fine-tuning"
---
<!--
#################################################
### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
#################################################
# file to edit: docs_src/text.ipynb
# instructions: https://docs.fast.ai/gen_doc_main.html
-->
<div class="container" id="notebook-container">
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Text-models,-data,-and-training">Text models, data, and training<a class="anchor-link" href="#Text-models,-data,-and-training">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>The <a href="/text.html#text"><code>text</code></a> module of the fastai library contains all the necessary functions to define a Dataset suitable for the various NLP (Natural Language Processing) tasks and quickly generate models you can use for them. Specifically:</p>
<ul>
<li><a href="/text.transform.html#text.transform"><code>text.transform</code></a> contains all the scripts to preprocess your data, from raw text to token ids,</li>
<li><a href="/text.data.html#text.data"><code>text.data</code></a> contains the definition of <a href="/text.data.html#TextDataBunch"><code>TextDataBunch</code></a>, which the main class you'll need in NLP,</li>
<li><a href="/text.learner.html#text.learner"><code>text.learner</code></a> contains helper functions to quickly create a language model or an RNN classifier.</li>
</ul>
<p>Have a look at the links above for full details of the API of each module, of read on for a quick overview.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Quick-Start:-Training-an-IMDb-sentiment-model-with-ULMFiT">Quick Start: Training an IMDb sentiment model with <em>ULMFiT</em><a class="anchor-link" href="#Quick-Start:-Training-an-IMDb-sentiment-model-with-ULMFiT">¶</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Let's start with a quick end-to-end example of training a model. We'll train a sentiment classifier on a sample of the popular IMDb data, showing 4 steps:</p>
<ol>
<li>Reading and viewing the IMDb data</li>
<li>Getting your data ready for modeling</li>
<li>Fine-tuning a language model</li>
<li>Building a classifier</li>
</ol>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Reading-and-viewing-the-IMDb-data">Reading and viewing the IMDb data<a class="anchor-link" href="#Reading-and-viewing-the-IMDb-data">¶</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>First let's import everything we need for text.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">fastai.text</span> <span class="k">import</span> <span class="o">*</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Contrary to images in Computer Vision, text can't directly be transformed into numbers to be fed into a model. The first thing we need to do is to preprocess our data so that we change the raw texts to lists of words, or tokens (a step that is called tokenization) then transform these tokens into numbers (a step that is called numericalization). These numbers are then passed to embedding layers that will convert them in arrays of floats before passing them through a model.</p>
<p>You can find on the web plenty of <a href="https://en.wikipedia.org/wiki/Word_embedding">Word Embeddings</a> to directly convert your tokens into floats. Those word embeddings have generally be trained on a large corpus such as wikipedia. Following the work of <a href="https://arxiv.org/abs/1801.06146">ULMFiT</a>, the fastai library is more focused on using pre-trained Language Models and fine-tuning them. Word embeddings are just vectors of 300 or 400 floats that represent different words, but a pretrained language model not only has those, but has also been trained to get a representation of full sentences and documents.</p>
<p>That's why the library is structured around three steps:</p>
<ol>
<li>Get your data preprocessed and ready to use in a minimum amount of code,</li>
<li>Create a language model with pretrained weights that you can fine-tune to your dataset,</li>
<li>Create other models such as classifiers on top of the encoder of the language model.</li>
</ol>
<p>To show examples, we have provided a small sample of the <a href="https://www.imdb.com/interfaces/">IMDB dataset</a> which contains 1,000 reviews of movies with labels (positive or negative).</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">path</span> <span class="o">=</span> <span class="n">untar_data</span><span class="p">(</span><span class="n">URLs</span><span class="o">.</span><span class="n">IMDB_SAMPLE</span><span class="p">)</span>
<span class="n">path</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>PosixPath('/home/ubuntu/.fastai/data/imdb_sample')</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Creating a dataset from your raw texts is very simple if you have it in one of those ways</p>
<ul>
<li>organized it in folders in an ImageNet style</li>
<li>organized in a csv file with labels columns and a text columns</li>
</ul>
<p>Here, the sample from imdb is in a texts csv files that looks like this:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="o">/</span><span class="s1">'texts.csv'</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>label</th>
<th>text</th>
<th>is_valid</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>negative</td>
<td>Un-bleeping-believable! Meg Ryan doesn't even ...</td>
<td>False</td>
</tr>
<tr>
<th>1</th>
<td>positive</td>
<td>This is a extremely well-made film. The acting...</td>
<td>False</td>
</tr>
<tr>
<th>2</th>
<td>negative</td>
<td>Every once in a long while a movie will come a...</td>
<td>False</td>
</tr>
<tr>
<th>3</th>
<td>positive</td>
<td>Name just says it all. I watched this movie wi...</td>
<td>False</td>
</tr>
<tr>
<th>4</th>
<td>negative</td>
<td>This movie succeeds at being one of the most u...</td>
<td>False</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Getting-your-data-ready-for-modeling">Getting your data ready for modeling<a class="anchor-link" href="#Getting-your-data-ready-for-modeling">¶</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>To get a <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> quickly, there are also several factory methods depending on how our data is structured. They are all detailed in <a href="/text.data.html#text.data"><code>text.data</code></a>, here we'll use the method <code>from_csv</code> of the <a href="/text.data.html#TextLMDataBunch"><code>TextLMDataBunch</code></a> (to get the data ready for a language model) and <a href="/text.data.html#TextClasDataBunch"><code>TextClasDataBunch</code></a> (to get the data ready for a text classifier) classes.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Language model data</span>
<span class="n">data_lm</span> <span class="o">=</span> <span class="n">TextLMDataBunch</span><span class="o">.</span><span class="n">from_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'texts.csv'</span><span class="p">)</span>
<span class="c1"># Classifier model data</span>
<span class="n">data_clas</span> <span class="o">=</span> <span class="n">TextClasDataBunch</span><span class="o">.</span><span class="n">from_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'texts.csv'</span><span class="p">,</span> <span class="n">vocab</span><span class="o">=</span><span class="n">data_lm</span><span class="o">.</span><span class="n">train_ds</span><span class="o">.</span><span class="n">vocab</span><span class="p">,</span> <span class="n">bs</span><span class="o">=</span><span class="mi">32</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>This does all the necessary preprocessing behind the scene. For the classifier, we also pass the vocabulary (mapping from ids to words) that we want to use: this is to ensure that <code>data_clas</code> will use the same dictionary as <code>data_lm</code>.</p>
<p>Since this step can be a bit time-consuming, it's best to save the result with:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_lm</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'data_lm_export.pkl'</span><span class="p">)</span>
<span class="n">data_clas</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'data_clas_export.pkl'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>This will create a 'tmp' directory where all the computed stuff will be stored. You can then reload those results with:</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_lm</span> <span class="o">=</span> <span class="n">load_data</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'data_lm_export.pkl'</span><span class="p">)</span>
<span class="n">data_clas</span> <span class="o">=</span> <span class="n">load_data</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'data_clas_export.pkl'</span><span class="p">,</span> <span class="n">bs</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Note that you can load the data with different <a href="/basic_data.html#DataBunch"><code>DataBunch</code></a> parameters (batch size, <code>bptt</code>,...)</p>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Fine-tuning-a-language-model">Fine-tuning a language model<a class="anchor-link" href="#Fine-tuning-a-language-model">¶</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>We can use the <code>data_lm</code> object we created earlier to fine-tune a pretrained language model. <a href="http://www.fast.ai/">fast.ai</a> has an English model with an AWD-LSTM architecture available that we can download. We can create a learner object that will directly create a model, download the pretrained weights and be ready for fine-tuning.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">language_model_learner</span><span class="p">(</span><span class="n">data_lm</span><span class="p">,</span> <span class="n">AWD_LSTM</span><span class="p">,</span> <span class="n">drop_mult</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">learn</span><span class="o">.</span><span class="n">fit_one_cycle</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1e-2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: left;">
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>4.319174</td>
<td>3.882361</td>
<td>0.288155</td>
<td>00:13</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Like a computer vision model, we can then unfreeze the model and fine-tune it.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">unfreeze</span><span class="p">()</span>
<span class="n">learn</span><span class="o">.</span><span class="n">fit_one_cycle</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1e-3</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: left;">
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>3.935607</td>
<td>3.811058</td>
<td>0.297262</td>
<td>00:16</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>To evaluate your language model, you can run the <a href="/basic_train.html#Learner.predict"><code>Learner.predict</code></a> method and specify the number of words you want it to guess.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="s2">"This is a review about"</span><span class="p">,</span> <span class="n">n_words</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>'This is a review about what was worth a word of reading , where some'</pre>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>It doesn't make much sense (we have a tiny vocabulary here and didn't train much on it) but note that it respects basic grammar (which comes from the pretrained model).</p>
<p>Finally we save the encoder to be able to use it for classification in the next section.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">save_encoder</span><span class="p">(</span><span class="s1">'ft_enc'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Building-a-classifier">Building a classifier<a class="anchor-link" href="#Building-a-classifier">¶</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>We now use the <code>data_clas</code> object we created earlier to build a classifier with our fine-tuned encoder. The learner object can be done in a single line.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span> <span class="o">=</span> <span class="n">text_classifier_learner</span><span class="p">(</span><span class="n">data_clas</span><span class="p">,</span> <span class="n">AWD_LSTM</span><span class="p">,</span> <span class="n">drop_mult</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">learn</span><span class="o">.</span><span class="n">load_encoder</span><span class="p">(</span><span class="s1">'ft_enc'</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data_clas</span><span class="o">.</span><span class="n">show_batch</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>text</th>
<th>target</th>
</tr>
</thead>
<tbody>
<tr>
<td>xxbos xxmaj raising xxmaj victor xxmaj vargas : a xxmaj review \n \n xxmaj you know , xxmaj raising xxmaj victor xxmaj vargas is like sticking your hands into a big , steaming bowl of xxunk . xxmaj it 's warm and gooey , but you 're not sure if it feels right . xxmaj try as i might , no matter how warm and gooey xxmaj raising xxmaj</td>
<td>negative</td>
</tr>
<tr>
<td>xxbos xxup the xxup shop xxup around xxup the xxup corner is one of the xxunk and most feel - good romantic comedies ever made . xxmaj there 's just no getting around that , and it 's hard to actually put one 's feeling for this film into words . xxmaj it 's not one of those films that tries too hard , nor does it come up with</td>
<td>positive</td>
</tr>
<tr>
<td>xxbos xxmaj now that xxmaj che(2008 ) has finished its relatively short xxmaj australian cinema run ( extremely limited xxunk screen in xxmaj sydney , after xxunk ) , i can xxunk join both xxunk of " xxmaj at xxmaj the xxmaj movies " in taking xxmaj steven xxmaj soderbergh to task . \n \n xxmaj it 's usually satisfying to watch a film director change his style /</td>
<td>negative</td>
</tr>
<tr>
<td>xxbos xxmaj this film sat on my xxmaj tivo for weeks before i watched it . i dreaded a self - indulgent xxunk flick about relationships gone bad . i was wrong ; this was an xxunk xxunk into the screwed - up xxunk of xxmaj new xxmaj yorkers . \n \n xxmaj the format is the same as xxmaj max xxmaj xxunk ' " xxmaj la xxmaj ronde</td>
<td>positive</td>
</tr>
<tr>
<td>xxbos i really wanted to love this show . i truly , honestly did . \n \n xxmaj for the first time , gay viewers get their own version of the " xxmaj the xxmaj bachelor " . xxmaj with the help of his obligatory " hag " xxmaj xxunk , xxmaj james , a good looking , well - to - do thirty - something has the chance</td>
<td>negative</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">fit_one_cycle</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">1e-2</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: left;">
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>0.603350</td>
<td>0.531586</td>
<td>0.741294</td>
<td>00:23</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Again, we can unfreeze the model and fine-tune it.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">freeze_to</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
<span class="n">learn</span><span class="o">.</span><span class="n">fit_one_cycle</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">slice</span><span class="p">(</span><span class="mf">5e-3</span><span class="o">/</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">5e-3</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: left;">
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>0.503833</td>
<td>0.444084</td>
<td>0.800995</td>
<td>00:29</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">unfreeze</span><span class="p">()</span>
<span class="n">learn</span><span class="o">.</span><span class="n">fit_one_cycle</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">slice</span><span class="p">(</span><span class="mf">2e-3</span><span class="o">/</span><span class="mi">100</span><span class="p">,</span> <span class="mf">2e-3</span><span class="p">))</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_html rendered_html output_subarea ">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: left;">
<th>epoch</th>
<th>train_loss</th>
<th>valid_loss</th>
<th>accuracy</th>
<th>time</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>0.410422</td>
<td>0.363226</td>
<td>0.850746</td>
<td>00:42</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Again, we can predict on a raw text by using the <a href="/basic_train.html#Learner.predict"><code>Learner.predict</code></a> method.</p>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">learn</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="s2">"This was a great movie!"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="output_text output_subarea output_execute_result">
<pre>(Category positive, tensor(1), tensor([0.0049, 0.9951]))</pre>
</div>
</div>
</div>
</div>
</div>
</div>