/
ner.html
367 lines (348 loc) · 19.1 KB
/
ner.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
<!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 0.10.0" />
<title>ktrain.text.shallownlp.ner 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.shallownlp.ner</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .imports import *
class NER:
def __init__(self, lang="en", predictor_path=None):
"""
```
pretrained NER.
Only English and Chinese are currenty supported.
Args:
lang(str): Currently, one of {'en', 'zh', 'ru'}: en=English , zh=Chinese, or ru=Russian
```
"""
if lang is None:
raise ValueError(
'lang is required (e.g., "en" for English, "zh" for Chinese, "ru" for Russian, etc.'
)
if predictor_path is None and lang not in ["en", "zh", "ru"]:
raise ValueError(
"Unsupported language: if predictor_path is None, then lang must be "
+ "'en' for English, 'zh' for Chinese, or 'ru' for Chinese"
)
self.lang = lang
if os.environ.get("DISABLE_V2_BEHAVIOR", None) != "1":
warnings.warn(
"Please add os.environ['DISABLE_V2_BEHAVIOR'] = '1' at top of your script or notebook"
)
msg = (
"\nNER in ktrain uses the CRF module from keras_contrib, which is not yet\n"
+ "fully compatible with TensorFlow 2. To use NER, you must add the following to the top of your\n"
+ "script or notebook BEFORE you import ktrain (after restarting runtime):\n\n"
+ "import os\n"
+ "os.environ['DISABLE_V2_BEHAVIOR'] = '1'\n"
)
print(msg)
return
else:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
if predictor_path is None and self.lang == "zh":
dirpath = os.path.dirname(os.path.abspath(__file__))
fpath = os.path.join(dirpath, "ner_models/ner_chinese")
elif predictor_path is None and self.lang == "ru":
dirpath = os.path.dirname(os.path.abspath(__file__))
fpath = os.path.join(dirpath, "ner_models/ner_russian")
elif predictor_path is None and self.lang == "en":
dirpath = os.path.dirname(os.path.abspath(__file__))
fpath = os.path.join(dirpath, "ner_models/ner_english")
elif predictor_path is None:
raise ValueError(
"Unsupported language: if predictor_path is None, then lang must be "
+ "'en' for English, 'zh' for Chinese, or 'ru' for Chinese"
)
else:
if not os.path.isfile(predictor_path) or not os.path.isfile(
predictor_path + ".preproc"
):
raise ValueError(
"could not find a valid predictor model "
+ "%s or valid Preprocessor %s at specified path"
% (predictor_path, predictor_path + ".preproc")
)
fpath = predictor_path
try:
import io
from contextlib import redirect_stdout
f = io.StringIO()
with redirect_stdout(f):
import ktrain
except:
raise ValueError(
"ktrain could not be imported. Install with: pip install ktrain"
)
self.predictor = ktrain.load_predictor(fpath)
def predict(self, texts, merge_tokens=True, batch_size=32):
"""
```
Extract named entities from supplied text
Args:
texts (list of str or str): list of texts to annotate
merge_tokens(bool): If True, tokens will be merged together by the entity
to which they are associated:
('Paul', 'B-PER'), ('Newman', 'I-PER') becomes ('Paul Newman', 'PER')
batch_size(int): Batch size to use for predictions (default:32)
```
"""
if isinstance(texts, str):
texts = [texts]
self.predictor.batch_size = batch_size
texts = [t.strip() for t in texts]
results = self.predictor.predict(texts, merge_tokens=merge_tokens)
if len(results) == 1:
results = results[0]
return results
# 2020-04-30: moved to text.ner.predictor
# def merge_tokens(self, annotated_sentence):
# if self.lang.startswith('zh'):
# sep = ''
# else:
# sep = ' '
# current_token = ""
# current_tag = ""
# entities = []
# for tup in annotated_sentence:
# token = tup[0]
# entity = tup[1]
# tag = entity.split('-')[1] if '-' in entity else None
# prefix = entity.split('-')[0] if '-' in entity else None
# # not within entity
# if tag is None and not current_token:
# continue
# # beginning of entity
# #elif tag and prefix=='B':
# elif tag and (prefix=='B' or prefix=='I' and not current_token):
# if current_token: # consecutive entities
# entities.append((current_token, current_tag))
# current_token = ""
# current_tag = None
# current_token = token
# current_tag = tag
# # end of entity
# elif tag is None and current_token:
# entities.append((current_token, current_tag))
# current_token = ""
# current_tag = None
# continue
# # within entity
# elif tag and current_token: # prefix I
# current_token = current_token + sep + token
# current_tag = tag
# return entities</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.text.shallownlp.ner.NER"><code class="flex name class">
<span>class <span class="ident">NER</span></span>
<span>(</span><span>lang='en', predictor_path=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>pretrained NER.
Only English and Chinese are currenty supported.
Args:
lang(str): Currently, one of {'en', 'zh', 'ru'}: en=English , zh=Chinese, or ru=Russian
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class NER:
def __init__(self, lang="en", predictor_path=None):
"""
```
pretrained NER.
Only English and Chinese are currenty supported.
Args:
lang(str): Currently, one of {'en', 'zh', 'ru'}: en=English , zh=Chinese, or ru=Russian
```
"""
if lang is None:
raise ValueError(
'lang is required (e.g., "en" for English, "zh" for Chinese, "ru" for Russian, etc.'
)
if predictor_path is None and lang not in ["en", "zh", "ru"]:
raise ValueError(
"Unsupported language: if predictor_path is None, then lang must be "
+ "'en' for English, 'zh' for Chinese, or 'ru' for Chinese"
)
self.lang = lang
if os.environ.get("DISABLE_V2_BEHAVIOR", None) != "1":
warnings.warn(
"Please add os.environ['DISABLE_V2_BEHAVIOR'] = '1' at top of your script or notebook"
)
msg = (
"\nNER in ktrain uses the CRF module from keras_contrib, which is not yet\n"
+ "fully compatible with TensorFlow 2. To use NER, you must add the following to the top of your\n"
+ "script or notebook BEFORE you import ktrain (after restarting runtime):\n\n"
+ "import os\n"
+ "os.environ['DISABLE_V2_BEHAVIOR'] = '1'\n"
)
print(msg)
return
else:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
if predictor_path is None and self.lang == "zh":
dirpath = os.path.dirname(os.path.abspath(__file__))
fpath = os.path.join(dirpath, "ner_models/ner_chinese")
elif predictor_path is None and self.lang == "ru":
dirpath = os.path.dirname(os.path.abspath(__file__))
fpath = os.path.join(dirpath, "ner_models/ner_russian")
elif predictor_path is None and self.lang == "en":
dirpath = os.path.dirname(os.path.abspath(__file__))
fpath = os.path.join(dirpath, "ner_models/ner_english")
elif predictor_path is None:
raise ValueError(
"Unsupported language: if predictor_path is None, then lang must be "
+ "'en' for English, 'zh' for Chinese, or 'ru' for Chinese"
)
else:
if not os.path.isfile(predictor_path) or not os.path.isfile(
predictor_path + ".preproc"
):
raise ValueError(
"could not find a valid predictor model "
+ "%s or valid Preprocessor %s at specified path"
% (predictor_path, predictor_path + ".preproc")
)
fpath = predictor_path
try:
import io
from contextlib import redirect_stdout
f = io.StringIO()
with redirect_stdout(f):
import ktrain
except:
raise ValueError(
"ktrain could not be imported. Install with: pip install ktrain"
)
self.predictor = ktrain.load_predictor(fpath)
def predict(self, texts, merge_tokens=True, batch_size=32):
"""
```
Extract named entities from supplied text
Args:
texts (list of str or str): list of texts to annotate
merge_tokens(bool): If True, tokens will be merged together by the entity
to which they are associated:
('Paul', 'B-PER'), ('Newman', 'I-PER') becomes ('Paul Newman', 'PER')
batch_size(int): Batch size to use for predictions (default:32)
```
"""
if isinstance(texts, str):
texts = [texts]
self.predictor.batch_size = batch_size
texts = [t.strip() for t in texts]
results = self.predictor.predict(texts, merge_tokens=merge_tokens)
if len(results) == 1:
results = results[0]
return results</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.shallownlp.ner.NER.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, texts, merge_tokens=True, batch_size=32)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Extract named entities from supplied text
Args:
texts (list of str or str): list of texts to annotate
merge_tokens(bool): If True, tokens will be merged together by the entity
to which they are associated:
('Paul', 'B-PER'), ('Newman', 'I-PER') becomes ('Paul Newman', 'PER')
batch_size(int): Batch size to use for predictions (default:32)
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, texts, merge_tokens=True, batch_size=32):
"""
```
Extract named entities from supplied text
Args:
texts (list of str or str): list of texts to annotate
merge_tokens(bool): If True, tokens will be merged together by the entity
to which they are associated:
('Paul', 'B-PER'), ('Newman', 'I-PER') becomes ('Paul Newman', 'PER')
batch_size(int): Batch size to use for predictions (default:32)
```
"""
if isinstance(texts, str):
texts = [texts]
self.predictor.batch_size = batch_size
texts = [t.strip() for t in texts]
results = self.predictor.predict(texts, merge_tokens=merge_tokens)
if len(results) == 1:
results = results[0]
return results</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</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.shallownlp" href="index.html">ktrain.text.shallownlp</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.text.shallownlp.ner.NER" href="#ktrain.text.shallownlp.ner.NER">NER</a></code></h4>
<ul class="">
<li><code><a title="ktrain.text.shallownlp.ner.NER.predict" href="#ktrain.text.shallownlp.ner.NER.predict">predict</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
</footer>
</body>
</html>