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
|
%global _empty_manifest_terminate_build 0
Name: python-laserembeddings
Version: 1.1.2
Release: 1
Summary: Production-ready LASER multilingual embeddings
License: BSD-3-Clause
URL: https://github.com/yannvgn/laserembeddings
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d1/d4/334569ff2a318e8d587506d4dd1b54260b2391a5759e0614326bc17969bc/laserembeddings-1.1.2.tar.gz
BuildArch: noarch
Requires: python3-torch
Requires: python3-subword-nmt
Requires: python3-numpy
Requires: python3-sacremoses
Requires: python3-transliterate
Requires: python3-mecab-python3
Requires: python3-ipadic
Requires: python3-jieba
%description
# LASER embeddings
[](https://github.com/yannvgn/laserembeddings/actions)
[](https://pypi.org/project/laserembeddings/)
[](https://github.com/yannvgn/laserembeddings/blob/master/LICENSE)
**Out-of-the-box multilingual sentence embeddings.**
laserembeddings is a pip-packaged, production-ready port of Facebook Research's [LASER](https://github.com/facebookresearch/LASER) (Language-Agnostic SEntence Representations) to compute multilingual sentence embeddings.
**Have a look at the project's repo ([master branch](https://github.com/yannvgn/laserembeddings) or [this release](https://github.com/yannvgn/laserembeddings/tree/v1.1.2)) for the full documentation.**
## Getting started
### Prerequisites
You'll need Python 3.6+ and PyTorch. Please refer to [PyTorch installation instructions](https://pytorch.org/get-started/locally/).
### Installation
```
pip install laserembeddings
```
#### Chinese language
Chinese is not supported by default. If you need to embed Chinese sentences, please install laserembeddings with the "zh" extra. This extra includes [jieba](https://github.com/fxsjy/jieba).
```
pip install laserembeddings[zh]
```
#### Japanese language
Japanese is not supported by default. If you need to embed Japanese sentences, please install laserembeddings with the "ja" extra. This extra includes [mecab-python3](https://github.com/SamuraiT/mecab-python3) and the [ipadic](https://github.com/polm/ipadic-py) dictionary, which is used in the original LASER project.
If you have issues running laserembeddings on Japanese sentences, please refer to [mecab-python3 documentation](https://github.com/SamuraiT/mecab-python3) for troubleshooting.
```
pip install laserembeddings[ja]
```
### Downloading the pre-trained models
```
python -m laserembeddings download-models
```
This will download the models to the default `data` directory next to the source code of the package. Use `python -m laserembeddings download-models path/to/model/directory` to download the models to a specific location.
### Usage
```python
from laserembeddings import Laser
laser = Laser()
# if all sentences are in the same language:
embeddings = laser.embed_sentences(
['let your neural network be polyglot',
'use multilingual embeddings!'],
lang='en') # lang is only used for tokenization
# embeddings is a N*1024 (N = number of sentences) NumPy array
```
If the sentences are not in the same language, you can pass a list of language codes:
```python
embeddings = laser.embed_sentences(
['I love pasta.',
"J'adore les pâtes.",
'Ich liebe Pasta.'],
lang=['en', 'fr', 'de'])
```
If you downloaded the models into a specific directory:
```python
from laserembeddings import Laser
path_to_bpe_codes = ...
path_to_bpe_vocab = ...
path_to_encoder = ...
laser = Laser(path_to_bpe_codes, path_to_bpe_vocab, path_to_encoder)
# you can also supply file objects instead of file paths
```
If you want to pull the models from S3:
```python
from io import BytesIO, StringIO
from laserembeddings import Laser
import boto3
s3 = boto3.resource('s3')
MODELS_BUCKET = ...
f_bpe_codes = StringIO(s3.Object(MODELS_BUCKET, 'path_to_bpe_codes.fcodes').get()['Body'].read().decode('utf-8'))
f_bpe_vocab = StringIO(s3.Object(MODELS_BUCKET, 'path_to_bpe_vocabulary.fvocab').get()['Body'].read().decode('utf-8'))
f_encoder = BytesIO(s3.Object(MODELS_BUCKET, 'path_to_encoder.pt').get()['Body'].read())
laser = Laser(f_bpe_codes, f_bpe_vocab, f_encoder)
```
%package -n python3-laserembeddings
Summary: Production-ready LASER multilingual embeddings
Provides: python-laserembeddings
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-laserembeddings
# LASER embeddings
[](https://github.com/yannvgn/laserembeddings/actions)
[](https://pypi.org/project/laserembeddings/)
[](https://github.com/yannvgn/laserembeddings/blob/master/LICENSE)
**Out-of-the-box multilingual sentence embeddings.**
laserembeddings is a pip-packaged, production-ready port of Facebook Research's [LASER](https://github.com/facebookresearch/LASER) (Language-Agnostic SEntence Representations) to compute multilingual sentence embeddings.
**Have a look at the project's repo ([master branch](https://github.com/yannvgn/laserembeddings) or [this release](https://github.com/yannvgn/laserembeddings/tree/v1.1.2)) for the full documentation.**
## Getting started
### Prerequisites
You'll need Python 3.6+ and PyTorch. Please refer to [PyTorch installation instructions](https://pytorch.org/get-started/locally/).
### Installation
```
pip install laserembeddings
```
#### Chinese language
Chinese is not supported by default. If you need to embed Chinese sentences, please install laserembeddings with the "zh" extra. This extra includes [jieba](https://github.com/fxsjy/jieba).
```
pip install laserembeddings[zh]
```
#### Japanese language
Japanese is not supported by default. If you need to embed Japanese sentences, please install laserembeddings with the "ja" extra. This extra includes [mecab-python3](https://github.com/SamuraiT/mecab-python3) and the [ipadic](https://github.com/polm/ipadic-py) dictionary, which is used in the original LASER project.
If you have issues running laserembeddings on Japanese sentences, please refer to [mecab-python3 documentation](https://github.com/SamuraiT/mecab-python3) for troubleshooting.
```
pip install laserembeddings[ja]
```
### Downloading the pre-trained models
```
python -m laserembeddings download-models
```
This will download the models to the default `data` directory next to the source code of the package. Use `python -m laserembeddings download-models path/to/model/directory` to download the models to a specific location.
### Usage
```python
from laserembeddings import Laser
laser = Laser()
# if all sentences are in the same language:
embeddings = laser.embed_sentences(
['let your neural network be polyglot',
'use multilingual embeddings!'],
lang='en') # lang is only used for tokenization
# embeddings is a N*1024 (N = number of sentences) NumPy array
```
If the sentences are not in the same language, you can pass a list of language codes:
```python
embeddings = laser.embed_sentences(
['I love pasta.',
"J'adore les pâtes.",
'Ich liebe Pasta.'],
lang=['en', 'fr', 'de'])
```
If you downloaded the models into a specific directory:
```python
from laserembeddings import Laser
path_to_bpe_codes = ...
path_to_bpe_vocab = ...
path_to_encoder = ...
laser = Laser(path_to_bpe_codes, path_to_bpe_vocab, path_to_encoder)
# you can also supply file objects instead of file paths
```
If you want to pull the models from S3:
```python
from io import BytesIO, StringIO
from laserembeddings import Laser
import boto3
s3 = boto3.resource('s3')
MODELS_BUCKET = ...
f_bpe_codes = StringIO(s3.Object(MODELS_BUCKET, 'path_to_bpe_codes.fcodes').get()['Body'].read().decode('utf-8'))
f_bpe_vocab = StringIO(s3.Object(MODELS_BUCKET, 'path_to_bpe_vocabulary.fvocab').get()['Body'].read().decode('utf-8'))
f_encoder = BytesIO(s3.Object(MODELS_BUCKET, 'path_to_encoder.pt').get()['Body'].read())
laser = Laser(f_bpe_codes, f_bpe_vocab, f_encoder)
```
%package help
Summary: Development documents and examples for laserembeddings
Provides: python3-laserembeddings-doc
%description help
# LASER embeddings
[](https://github.com/yannvgn/laserembeddings/actions)
[](https://pypi.org/project/laserembeddings/)
[](https://github.com/yannvgn/laserembeddings/blob/master/LICENSE)
**Out-of-the-box multilingual sentence embeddings.**
laserembeddings is a pip-packaged, production-ready port of Facebook Research's [LASER](https://github.com/facebookresearch/LASER) (Language-Agnostic SEntence Representations) to compute multilingual sentence embeddings.
**Have a look at the project's repo ([master branch](https://github.com/yannvgn/laserembeddings) or [this release](https://github.com/yannvgn/laserembeddings/tree/v1.1.2)) for the full documentation.**
## Getting started
### Prerequisites
You'll need Python 3.6+ and PyTorch. Please refer to [PyTorch installation instructions](https://pytorch.org/get-started/locally/).
### Installation
```
pip install laserembeddings
```
#### Chinese language
Chinese is not supported by default. If you need to embed Chinese sentences, please install laserembeddings with the "zh" extra. This extra includes [jieba](https://github.com/fxsjy/jieba).
```
pip install laserembeddings[zh]
```
#### Japanese language
Japanese is not supported by default. If you need to embed Japanese sentences, please install laserembeddings with the "ja" extra. This extra includes [mecab-python3](https://github.com/SamuraiT/mecab-python3) and the [ipadic](https://github.com/polm/ipadic-py) dictionary, which is used in the original LASER project.
If you have issues running laserembeddings on Japanese sentences, please refer to [mecab-python3 documentation](https://github.com/SamuraiT/mecab-python3) for troubleshooting.
```
pip install laserembeddings[ja]
```
### Downloading the pre-trained models
```
python -m laserembeddings download-models
```
This will download the models to the default `data` directory next to the source code of the package. Use `python -m laserembeddings download-models path/to/model/directory` to download the models to a specific location.
### Usage
```python
from laserembeddings import Laser
laser = Laser()
# if all sentences are in the same language:
embeddings = laser.embed_sentences(
['let your neural network be polyglot',
'use multilingual embeddings!'],
lang='en') # lang is only used for tokenization
# embeddings is a N*1024 (N = number of sentences) NumPy array
```
If the sentences are not in the same language, you can pass a list of language codes:
```python
embeddings = laser.embed_sentences(
['I love pasta.',
"J'adore les pâtes.",
'Ich liebe Pasta.'],
lang=['en', 'fr', 'de'])
```
If you downloaded the models into a specific directory:
```python
from laserembeddings import Laser
path_to_bpe_codes = ...
path_to_bpe_vocab = ...
path_to_encoder = ...
laser = Laser(path_to_bpe_codes, path_to_bpe_vocab, path_to_encoder)
# you can also supply file objects instead of file paths
```
If you want to pull the models from S3:
```python
from io import BytesIO, StringIO
from laserembeddings import Laser
import boto3
s3 = boto3.resource('s3')
MODELS_BUCKET = ...
f_bpe_codes = StringIO(s3.Object(MODELS_BUCKET, 'path_to_bpe_codes.fcodes').get()['Body'].read().decode('utf-8'))
f_bpe_vocab = StringIO(s3.Object(MODELS_BUCKET, 'path_to_bpe_vocabulary.fvocab').get()['Body'].read().decode('utf-8'))
f_encoder = BytesIO(s3.Object(MODELS_BUCKET, 'path_to_encoder.pt').get()['Body'].read())
laser = Laser(f_bpe_codes, f_bpe_vocab, f_encoder)
```
%prep
%autosetup -n laserembeddings-1.1.2
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-laserembeddings -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.2-1
- Package Spec generated
|
