/
data.html
690 lines (619 loc) · 31.2 KB
/
data.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
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.9.2" />
<title>ktrain.text.ner.data API documentation</title>
<meta name="description" content="" />
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.text.ner.data</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from ...imports import *
from ... import utils as U
from .. import textutils as TU
from . import preprocessor as pp
from .preprocessor import NERPreprocessor
from .anago.preprocessing import IndexTransformer
MAXLEN = 128
WORD_COL = pp.WORD_COL
TAG_COL = pp.TAG_COL
SENT_COL = pp.SENT_COL
def entities_from_gmb(train_filepath,
val_filepath=None,
use_char=False,
word_column=WORD_COL,
tag_column=TAG_COL,
sentence_column=SENT_COL,
encoding=None,
val_pct=0.1, verbose=1):
"""
Loads sequence-labeled data from text file in the Groningen
Meaning Bank (GMB) format.
"""
return entities_from_txt(train_filepath=train_filepath,
val_filepath=val_filepath,
use_char=use_char,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
data_format='gmb',
encoding=encoding,
val_pct=val_pct, verbose=verbose)
def entities_from_conll2003(train_filepath,
val_filepath=None,
use_char=False,
encoding=None,
val_pct=0.1, verbose=1):
"""
Loads sequence-labeled data from a file in CoNLL2003 format.
"""
return entities_from_txt(train_filepath=train_filepath,
val_filepath=val_filepath,
use_char=use_char,
data_format='conll2003',
encoding=encoding,
val_pct=val_pct, verbose=verbose)
def entities_from_txt(train_filepath,
val_filepath=None,
use_char=False,
word_column=WORD_COL,
tag_column=TAG_COL,
sentence_column=SENT_COL,
data_format='conll2003',
encoding=None,
val_pct=0.1, verbose=1):
"""
Loads sequence-labeled data from comma or tab-delmited text file.
Format of file is either the CoNLL2003 format or Groningen Meaning
Bank (GMB) format - specified with data_format parameter.
In both formats, each word appars on a separate line along with
its associated tag (or label).
The last item on each line should be the tag or label assigned to word.
In the CoNLL2003 format, there is an empty line after
each sentence. In the GMB format, sentences are deliniated
with a third column denoting the Sentence ID.
More information on CoNLL2003 format:
https://www.aclweb.org/anthology/W03-0419
CoNLL Example (each column is typically separated by space or tab)
and no column headings:
Paul B-PER
Newman I-PER
is O
a O
great O
actor O
! O
More information on GMB format:
Refer to ner_dataset.csv on Kaggle here:
https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/version/2
GMB example (each column separated by comma or tab)
with column headings:
SentenceID Word Tag
1 Paul B-PER
1 Newman I-PER
1 is O
1 a O
1 great O
1 actor O
1 ! O
Args:
train_filepath(str): file path to training CSV
val_filepath (str): file path to validation dataset
use_char(bool): If True, data will be preprocessed to use character embeddings in addition to word embeddings
word_column(str): name of column containing the text
tag_column(str): name of column containing lael
sentence_column(str): name of column containing Sentence IDs
data_format(str): one of colnll2003 or gmb
word_column, tag_column, and sentence_column
ignored if 'conll2003'
encoding(str): the encoding to use. If None, encoding is discovered automatically
val_pct(float): Proportion of training to use for validation.
verbose (boolean): verbosity
"""
# set dataframe converter
if data_format == 'gmb':
data_to_df = pp.gmb_to_df
else:
data_to_df = pp.conll2003_to_df
word_column, tag_column, sentence_column = WORD_COL, TAG_COL, SENT_COL
# detect encoding
if encoding is None:
with open(train_filepath, 'rb') as f:
encoding = TU.detect_encoding(f.read())
U.vprint('detected encoding: %s (if wrong, set manually)' % (encoding), verbose=verbose)
# create dataframe
train_df = data_to_df(train_filepath, encoding=encoding)
val_df = None if val_filepath is None else data_to_df(val_filepath, encoding=encoding)
return entities_from_df(train_df,
val_df=val_df,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
use_char=use_char,
val_pct=val_pct, verbose=verbose)
def entities_from_df(train_df,
val_df=None,
word_column=WORD_COL,
tag_column=TAG_COL,
sentence_column=SENT_COL,
use_char=False,
val_pct=0.1, verbose=1):
"""
Load entities from pandas DataFrame
Args:
train_df(pd.DataFrame): training data
val_df(pdf.DataFrame): validation data
word_column(str): name of column containing the text
tag_column(str): name of column containing lael
sentence_column(str): name of column containing Sentence IDs
use_char(bool): If True, data will be preprocessed to use character embeddings in addition to word embeddings
verbose (boolean): verbosity
"""
# process dataframe and instantiate NERPreprocessor
x, y = pp.process_df(train_df,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
verbose=verbose)
# get validation set
if val_df is None:
x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size=val_pct)
else:
x_train, y_train = x, y
(x_valid, y_valid) = pp.process_df(val_df,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
verbose=0)
# preprocess and convert to generator
p = IndexTransformer(use_char=use_char)
preproc = NERPreprocessor(p)
preproc.fit(x_train, y_train)
trn = pp.NERSequence(x_train, y_train, batch_size=U.DEFAULT_BS, p=p)
val = pp.NERSequence(x_valid, y_valid, batch_size=U.DEFAULT_BS, p=p)
return ( trn, val, preproc)
def entities_from_array(x_train, y_train,
x_test=None, y_test=None,
use_char=False,
val_pct=0.1,
verbose=1):
"""
Load entities from arrays
Args:
x_train(list): list of list of entity tokens for training
Example: x_train = [['Hello', 'world'], ['Hello', 'Cher'], ['I', 'love', 'Chicago']]
y_train(list): list of list of tokens representing entity labels
Example: y_train = [['O', 'O'], ['O', 'B-PER'], ['O', 'O', 'B-LOC']]
x_test(list): list of list of entity tokens for validation
Example: x_train = [['Hello', 'world'], ['Hello', 'Cher'], ['I', 'love', 'Chicago']]
y_test(list): list of list of tokens representing entity labels
Example: y_train = [['O', 'O'], ['O', 'B-PER'], ['O', 'O', 'B-LOC']]
use_char(bool): If True, data will be preprocessed to use character embeddings in addition to word embeddings
val_pct(float): percentage of training to use for validation if no validation data is supplied
verbose (boolean): verbosity
"""
# TODO: converting to df to use entities_from_df - needs to be refactored
train_df = pp.array_to_df(x_train, y_train)
val_df = None
if x_test is not None and y_test is not None:
val_df = pp.array_to_df(x_test, y_test)
if verbose:
print('training data sample:')
print(train_df.head())
if val_df is not None:
print('validation data sample:')
print(val_df.head())
return entities_from_df(train_df, val_df=val_df, val_pct=val_pct,
use_char=use_char, verbose=verbose)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.text.ner.data.entities_from_array"><code class="name flex">
<span>def <span class="ident">entities_from_array</span></span>(<span>x_train, y_train, x_test=None, y_test=None, use_char=False, val_pct=0.1, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Load entities from arrays</p>
<h2 id="args">Args</h2>
<p>x_train(list): list of list of entity tokens for training
Example: x_train = [['Hello', 'world'], ['Hello', 'Cher'], ['I', 'love', 'Chicago']]
y_train(list): list of list of tokens representing entity labels
Example:
y_train = [['O', 'O'], ['O', 'B-PER'], ['O', 'O', 'B-LOC']]
x_test(list): list of list of entity tokens for validation
Example: x_train = [['Hello', 'world'], ['Hello', 'Cher'], ['I', 'love', 'Chicago']]
y_test(list): list of list of tokens representing entity labels
Example:
y_train = [['O', 'O'], ['O', 'B-PER'], ['O', 'O', 'B-LOC']]
use_char(bool):
If True, data will be preprocessed to use character embeddings
in addition to word embeddings
val_pct(float):
percentage of training to use for validation if no validation data is supplied
verbose (boolean): verbosity</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def entities_from_array(x_train, y_train,
x_test=None, y_test=None,
use_char=False,
val_pct=0.1,
verbose=1):
"""
Load entities from arrays
Args:
x_train(list): list of list of entity tokens for training
Example: x_train = [['Hello', 'world'], ['Hello', 'Cher'], ['I', 'love', 'Chicago']]
y_train(list): list of list of tokens representing entity labels
Example: y_train = [['O', 'O'], ['O', 'B-PER'], ['O', 'O', 'B-LOC']]
x_test(list): list of list of entity tokens for validation
Example: x_train = [['Hello', 'world'], ['Hello', 'Cher'], ['I', 'love', 'Chicago']]
y_test(list): list of list of tokens representing entity labels
Example: y_train = [['O', 'O'], ['O', 'B-PER'], ['O', 'O', 'B-LOC']]
use_char(bool): If True, data will be preprocessed to use character embeddings in addition to word embeddings
val_pct(float): percentage of training to use for validation if no validation data is supplied
verbose (boolean): verbosity
"""
# TODO: converting to df to use entities_from_df - needs to be refactored
train_df = pp.array_to_df(x_train, y_train)
val_df = None
if x_test is not None and y_test is not None:
val_df = pp.array_to_df(x_test, y_test)
if verbose:
print('training data sample:')
print(train_df.head())
if val_df is not None:
print('validation data sample:')
print(val_df.head())
return entities_from_df(train_df, val_df=val_df, val_pct=val_pct,
use_char=use_char, verbose=verbose)</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.data.entities_from_conll2003"><code class="name flex">
<span>def <span class="ident">entities_from_conll2003</span></span>(<span>train_filepath, val_filepath=None, use_char=False, encoding=None, val_pct=0.1, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Loads sequence-labeled data from a file in CoNLL2003 format.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def entities_from_conll2003(train_filepath,
val_filepath=None,
use_char=False,
encoding=None,
val_pct=0.1, verbose=1):
"""
Loads sequence-labeled data from a file in CoNLL2003 format.
"""
return entities_from_txt(train_filepath=train_filepath,
val_filepath=val_filepath,
use_char=use_char,
data_format='conll2003',
encoding=encoding,
val_pct=val_pct, verbose=verbose)</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.data.entities_from_df"><code class="name flex">
<span>def <span class="ident">entities_from_df</span></span>(<span>train_df, val_df=None, word_column='Word', tag_column='Tag', sentence_column='SentenceID', use_char=False, val_pct=0.1, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Load entities from pandas DataFrame</p>
<h2 id="args">Args</h2>
<dl>
<dt>train_df(pd.DataFrame): training data</dt>
<dt>val_df(pdf.DataFrame): validation data</dt>
<dt>word_column(str): name of column containing the text</dt>
<dt>tag_column(str): name of column containing lael</dt>
<dt>sentence_column(str): name of column containing Sentence IDs</dt>
<dt>use_char(bool):
If True, data will be preprocessed to use character embeddings
in addition to word embeddings</dt>
<dt><strong><code>verbose</code></strong> : <code>boolean</code></dt>
<dd>verbosity</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def entities_from_df(train_df,
val_df=None,
word_column=WORD_COL,
tag_column=TAG_COL,
sentence_column=SENT_COL,
use_char=False,
val_pct=0.1, verbose=1):
"""
Load entities from pandas DataFrame
Args:
train_df(pd.DataFrame): training data
val_df(pdf.DataFrame): validation data
word_column(str): name of column containing the text
tag_column(str): name of column containing lael
sentence_column(str): name of column containing Sentence IDs
use_char(bool): If True, data will be preprocessed to use character embeddings in addition to word embeddings
verbose (boolean): verbosity
"""
# process dataframe and instantiate NERPreprocessor
x, y = pp.process_df(train_df,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
verbose=verbose)
# get validation set
if val_df is None:
x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size=val_pct)
else:
x_train, y_train = x, y
(x_valid, y_valid) = pp.process_df(val_df,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
verbose=0)
# preprocess and convert to generator
p = IndexTransformer(use_char=use_char)
preproc = NERPreprocessor(p)
preproc.fit(x_train, y_train)
trn = pp.NERSequence(x_train, y_train, batch_size=U.DEFAULT_BS, p=p)
val = pp.NERSequence(x_valid, y_valid, batch_size=U.DEFAULT_BS, p=p)
return ( trn, val, preproc)</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.data.entities_from_gmb"><code class="name flex">
<span>def <span class="ident">entities_from_gmb</span></span>(<span>train_filepath, val_filepath=None, use_char=False, word_column='Word', tag_column='Tag', sentence_column='SentenceID', encoding=None, val_pct=0.1, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Loads sequence-labeled data from text file in the
Groningen
Meaning Bank
(GMB) format.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def entities_from_gmb(train_filepath,
val_filepath=None,
use_char=False,
word_column=WORD_COL,
tag_column=TAG_COL,
sentence_column=SENT_COL,
encoding=None,
val_pct=0.1, verbose=1):
"""
Loads sequence-labeled data from text file in the Groningen
Meaning Bank (GMB) format.
"""
return entities_from_txt(train_filepath=train_filepath,
val_filepath=val_filepath,
use_char=use_char,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
data_format='gmb',
encoding=encoding,
val_pct=val_pct, verbose=verbose)</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.data.entities_from_txt"><code class="name flex">
<span>def <span class="ident">entities_from_txt</span></span>(<span>train_filepath, val_filepath=None, use_char=False, word_column='Word', tag_column='Tag', sentence_column='SentenceID', data_format='conll2003', encoding=None, val_pct=0.1, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Loads sequence-labeled data from comma or tab-delmited text file.
Format of file is either the CoNLL2003 format or Groningen Meaning
Bank (GMB) format - specified with data_format parameter.</p>
<p>In both formats, each word appars on a separate line along with
its associated tag (or label).<br>
The last item on each line should be the tag or label assigned to word.</p>
<p>In the CoNLL2003 format, there is an empty line after
each sentence.
In the GMB format, sentences are deliniated
with a third column denoting the Sentence ID.</p>
<p>More information on CoNLL2003 format:
<a href="https://www.aclweb.org/anthology/W03-0419">https://www.aclweb.org/anthology/W03-0419</a></p>
<p>CoNLL Example (each column is typically separated by space or tab)
and
no column headings:</p>
<p>Paul
B-PER
Newman
I-PER
is
O
a
O
great
O
actor
O
!
O</p>
<p>More information on GMB format:
Refer to ner_dataset.csv on Kaggle here:
<a href="https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/version/2">https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/version/2</a></p>
<p>GMB example (each column separated by comma or tab)
with column headings:</p>
<p>SentenceID
Word
Tag
<br>
1
Paul
B-PER
1
Newman
I-PER
1
is
O
1
a
O
1
great
O
1
actor
O
1
!
O</p>
<h2 id="args">Args</h2>
<dl>
<dt>train_filepath(str): file path to training CSV</dt>
<dt><strong><code>val_filepath</code></strong> : <code>str</code></dt>
<dd>file path to validation dataset</dd>
<dt>use_char(bool):
If True, data will be preprocessed to use character embeddings in addition to word embeddings</dt>
<dt>word_column(str): name of column containing the text</dt>
<dt>tag_column(str): name of column containing lael</dt>
<dt>sentence_column(str): name of column containing Sentence IDs</dt>
<dt>data_format(str): one of colnll2003 or gmb</dt>
<dt>word_column, tag_column, and sentence_column</dt>
<dt>ignored if 'conll2003'</dt>
<dt>encoding(str): the encoding to use.
If None, encoding is discovered automatically</dt>
<dt>val_pct(float): Proportion of training to use for validation.</dt>
<dt><strong><code>verbose</code></strong> : <code>boolean</code></dt>
<dd>verbosity</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def entities_from_txt(train_filepath,
val_filepath=None,
use_char=False,
word_column=WORD_COL,
tag_column=TAG_COL,
sentence_column=SENT_COL,
data_format='conll2003',
encoding=None,
val_pct=0.1, verbose=1):
"""
Loads sequence-labeled data from comma or tab-delmited text file.
Format of file is either the CoNLL2003 format or Groningen Meaning
Bank (GMB) format - specified with data_format parameter.
In both formats, each word appars on a separate line along with
its associated tag (or label).
The last item on each line should be the tag or label assigned to word.
In the CoNLL2003 format, there is an empty line after
each sentence. In the GMB format, sentences are deliniated
with a third column denoting the Sentence ID.
More information on CoNLL2003 format:
https://www.aclweb.org/anthology/W03-0419
CoNLL Example (each column is typically separated by space or tab)
and no column headings:
Paul B-PER
Newman I-PER
is O
a O
great O
actor O
! O
More information on GMB format:
Refer to ner_dataset.csv on Kaggle here:
https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/version/2
GMB example (each column separated by comma or tab)
with column headings:
SentenceID Word Tag
1 Paul B-PER
1 Newman I-PER
1 is O
1 a O
1 great O
1 actor O
1 ! O
Args:
train_filepath(str): file path to training CSV
val_filepath (str): file path to validation dataset
use_char(bool): If True, data will be preprocessed to use character embeddings in addition to word embeddings
word_column(str): name of column containing the text
tag_column(str): name of column containing lael
sentence_column(str): name of column containing Sentence IDs
data_format(str): one of colnll2003 or gmb
word_column, tag_column, and sentence_column
ignored if 'conll2003'
encoding(str): the encoding to use. If None, encoding is discovered automatically
val_pct(float): Proportion of training to use for validation.
verbose (boolean): verbosity
"""
# set dataframe converter
if data_format == 'gmb':
data_to_df = pp.gmb_to_df
else:
data_to_df = pp.conll2003_to_df
word_column, tag_column, sentence_column = WORD_COL, TAG_COL, SENT_COL
# detect encoding
if encoding is None:
with open(train_filepath, 'rb') as f:
encoding = TU.detect_encoding(f.read())
U.vprint('detected encoding: %s (if wrong, set manually)' % (encoding), verbose=verbose)
# create dataframe
train_df = data_to_df(train_filepath, encoding=encoding)
val_df = None if val_filepath is None else data_to_df(val_filepath, encoding=encoding)
return entities_from_df(train_df,
val_df=val_df,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
use_char=use_char,
val_pct=val_pct, verbose=verbose)</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.text.ner" href="index.html">ktrain.text.ner</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="ktrain.text.ner.data.entities_from_array" href="#ktrain.text.ner.data.entities_from_array">entities_from_array</a></code></li>
<li><code><a title="ktrain.text.ner.data.entities_from_conll2003" href="#ktrain.text.ner.data.entities_from_conll2003">entities_from_conll2003</a></code></li>
<li><code><a title="ktrain.text.ner.data.entities_from_df" href="#ktrain.text.ner.data.entities_from_df">entities_from_df</a></code></li>
<li><code><a title="ktrain.text.ner.data.entities_from_gmb" href="#ktrain.text.ner.data.entities_from_gmb">entities_from_gmb</a></code></li>
<li><code><a title="ktrain.text.ner.data.entities_from_txt" href="#ktrain.text.ner.data.entities_from_txt">entities_from_txt</a></code></li>
</ul>
</li>
</ul>
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.9.2</a>.</p>
</footer>
</body>
</html>