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|
%global _empty_manifest_terminate_build 0
Name: python-bert-embedding
Version: 1.0.1
Release: 1
Summary: BERT token level embedding with MxNet
License: ALv2
URL: https://github.com/imgarylai/bert_embedding
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/32/49/13f76cef121677994bb1b0e8baa8b8bf88405eb1be554925fe8682b7b71e/bert_embedding-1.0.1.tar.gz
BuildArch: noarch
Requires: python3-typing
Requires: python3-numpy
Requires: python3-mxnet
Requires: python3-gluonnlp
Requires: python3-mxnet-cu92
%description
# Bert Embeddings
[](https://travis-ci.org/imgarylai/bert-embedding) [](https://codecov.io/gh/imgarylai/bert-embedding) [](https://pypi.org/project/bert-embedding/) [](https://bert-embedding.readthedocs.io/en/latest/?badge=latest)
[BERT](https://arxiv.org/abs/1810.04805), published by [Google](https://github.com/google-research/bert), is new way to obtain pre-trained language model word representation. Many NLP tasks are benefit from BERT to get the SOTA.
The goal of this project is to obtain the token embedding from BERT's pre-trained model. In this way, instead of building and do fine-tuning for an end-to-end NLP model, you can build your model by just utilizing or token embedding.
This project is implemented with [@MXNet](https://github.com/apache/incubator-mxnet). Special thanks to [@gluon-nlp](https://github.com/dmlc/gluon-nlp) team.
## Install
```
pip install bert-embedding
# If you want to run on GPU machine, please install `mxnet-cu92`.
pip install mxnet-cu92
```
## Usage
```python
from bert_embedding import BertEmbedding
bert_abstract = """We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers.
As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
BERT is conceptually simple and empirically powerful.
It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE benchmark to 80.4% (7.6% absolute improvement), MultiNLI accuracy to 86.7 (5.6% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5% absolute improvement), outperforming human performance by 2.0%."""
sentences = bert_abstract.split('\n')
bert_embedding = BertEmbedding()
result = bert_embedding(sentences)
```
If you want to use GPU, please import mxnet and set context
```python
import mxnet as mx
from bert_embedding import BertEmbedding
...
ctx = mx.gpu(0)
bert = BertEmbedding(ctx=ctx)
```
This result is a list of a tuple containing (tokens, tokens embedding)
For example:
```python
first_sentence = result[0]
first_sentence[0]
# ['we', 'introduce', 'a', 'new', 'language', 'representation', 'model', 'called', 'bert', ',', 'which', 'stands', 'for', 'bidirectional', 'encoder', 'representations', 'from', 'transformers']
len(first_sentence[0])
# 18
len(first_sentence[1])
# 18
first_token_in_first_sentence = first_sentence[1]
first_token_in_first_sentence[1]
# array([ 0.4805648 , 0.18369392, -0.28554988, ..., -0.01961522,
# 1.0207764 , -0.67167974], dtype=float32)
first_token_in_first_sentence[1].shape
# (768,)
```
## OOV
There are three ways to handle oov, avg (default), sum, and last. This can be specified in encoding.
```python
...
bert_embedding = BertEmbedding()
bert_embedding(sentences, 'sum')
...
```
## Available pre-trained BERT models
| |book_corpus_wiki_en_uncased|book_corpus_wiki_en_cased|wiki_multilingual|wiki_multilingual_cased|wiki_cn|
|---|---|---|---|---|---|
|bert_12_768_12|✓|✓|✓|✓|✓|
|bert_24_1024_16|x|✓|x|x|x|
Example of using the large pre-trained BERT model from Google
```python
from bert_embedding import BertEmbedding
bert_embedding = BertEmbedding(model='bert_24_1024_16', dataset_name='book_corpus_wiki_en_cased')
```
Source: [gluonnlp](http://gluon-nlp.mxnet.io/model_zoo/bert/index.html)
%package -n python3-bert-embedding
Summary: BERT token level embedding with MxNet
Provides: python-bert-embedding
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-bert-embedding
# Bert Embeddings
[](https://travis-ci.org/imgarylai/bert-embedding) [](https://codecov.io/gh/imgarylai/bert-embedding) [](https://pypi.org/project/bert-embedding/) [](https://bert-embedding.readthedocs.io/en/latest/?badge=latest)
[BERT](https://arxiv.org/abs/1810.04805), published by [Google](https://github.com/google-research/bert), is new way to obtain pre-trained language model word representation. Many NLP tasks are benefit from BERT to get the SOTA.
The goal of this project is to obtain the token embedding from BERT's pre-trained model. In this way, instead of building and do fine-tuning for an end-to-end NLP model, you can build your model by just utilizing or token embedding.
This project is implemented with [@MXNet](https://github.com/apache/incubator-mxnet). Special thanks to [@gluon-nlp](https://github.com/dmlc/gluon-nlp) team.
## Install
```
pip install bert-embedding
# If you want to run on GPU machine, please install `mxnet-cu92`.
pip install mxnet-cu92
```
## Usage
```python
from bert_embedding import BertEmbedding
bert_abstract = """We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers.
As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
BERT is conceptually simple and empirically powerful.
It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE benchmark to 80.4% (7.6% absolute improvement), MultiNLI accuracy to 86.7 (5.6% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5% absolute improvement), outperforming human performance by 2.0%."""
sentences = bert_abstract.split('\n')
bert_embedding = BertEmbedding()
result = bert_embedding(sentences)
```
If you want to use GPU, please import mxnet and set context
```python
import mxnet as mx
from bert_embedding import BertEmbedding
...
ctx = mx.gpu(0)
bert = BertEmbedding(ctx=ctx)
```
This result is a list of a tuple containing (tokens, tokens embedding)
For example:
```python
first_sentence = result[0]
first_sentence[0]
# ['we', 'introduce', 'a', 'new', 'language', 'representation', 'model', 'called', 'bert', ',', 'which', 'stands', 'for', 'bidirectional', 'encoder', 'representations', 'from', 'transformers']
len(first_sentence[0])
# 18
len(first_sentence[1])
# 18
first_token_in_first_sentence = first_sentence[1]
first_token_in_first_sentence[1]
# array([ 0.4805648 , 0.18369392, -0.28554988, ..., -0.01961522,
# 1.0207764 , -0.67167974], dtype=float32)
first_token_in_first_sentence[1].shape
# (768,)
```
## OOV
There are three ways to handle oov, avg (default), sum, and last. This can be specified in encoding.
```python
...
bert_embedding = BertEmbedding()
bert_embedding(sentences, 'sum')
...
```
## Available pre-trained BERT models
| |book_corpus_wiki_en_uncased|book_corpus_wiki_en_cased|wiki_multilingual|wiki_multilingual_cased|wiki_cn|
|---|---|---|---|---|---|
|bert_12_768_12|✓|✓|✓|✓|✓|
|bert_24_1024_16|x|✓|x|x|x|
Example of using the large pre-trained BERT model from Google
```python
from bert_embedding import BertEmbedding
bert_embedding = BertEmbedding(model='bert_24_1024_16', dataset_name='book_corpus_wiki_en_cased')
```
Source: [gluonnlp](http://gluon-nlp.mxnet.io/model_zoo/bert/index.html)
%package help
Summary: Development documents and examples for bert-embedding
Provides: python3-bert-embedding-doc
%description help
# Bert Embeddings
[](https://travis-ci.org/imgarylai/bert-embedding) [](https://codecov.io/gh/imgarylai/bert-embedding) [](https://pypi.org/project/bert-embedding/) [](https://bert-embedding.readthedocs.io/en/latest/?badge=latest)
[BERT](https://arxiv.org/abs/1810.04805), published by [Google](https://github.com/google-research/bert), is new way to obtain pre-trained language model word representation. Many NLP tasks are benefit from BERT to get the SOTA.
The goal of this project is to obtain the token embedding from BERT's pre-trained model. In this way, instead of building and do fine-tuning for an end-to-end NLP model, you can build your model by just utilizing or token embedding.
This project is implemented with [@MXNet](https://github.com/apache/incubator-mxnet). Special thanks to [@gluon-nlp](https://github.com/dmlc/gluon-nlp) team.
## Install
```
pip install bert-embedding
# If you want to run on GPU machine, please install `mxnet-cu92`.
pip install mxnet-cu92
```
## Usage
```python
from bert_embedding import BertEmbedding
bert_abstract = """We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers.
As a result, the pre-trained BERT representations can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
BERT is conceptually simple and empirically powerful.
It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE benchmark to 80.4% (7.6% absolute improvement), MultiNLI accuracy to 86.7 (5.6% absolute improvement) and the SQuAD v1.1 question answering Test F1 to 93.2 (1.5% absolute improvement), outperforming human performance by 2.0%."""
sentences = bert_abstract.split('\n')
bert_embedding = BertEmbedding()
result = bert_embedding(sentences)
```
If you want to use GPU, please import mxnet and set context
```python
import mxnet as mx
from bert_embedding import BertEmbedding
...
ctx = mx.gpu(0)
bert = BertEmbedding(ctx=ctx)
```
This result is a list of a tuple containing (tokens, tokens embedding)
For example:
```python
first_sentence = result[0]
first_sentence[0]
# ['we', 'introduce', 'a', 'new', 'language', 'representation', 'model', 'called', 'bert', ',', 'which', 'stands', 'for', 'bidirectional', 'encoder', 'representations', 'from', 'transformers']
len(first_sentence[0])
# 18
len(first_sentence[1])
# 18
first_token_in_first_sentence = first_sentence[1]
first_token_in_first_sentence[1]
# array([ 0.4805648 , 0.18369392, -0.28554988, ..., -0.01961522,
# 1.0207764 , -0.67167974], dtype=float32)
first_token_in_first_sentence[1].shape
# (768,)
```
## OOV
There are three ways to handle oov, avg (default), sum, and last. This can be specified in encoding.
```python
...
bert_embedding = BertEmbedding()
bert_embedding(sentences, 'sum')
...
```
## Available pre-trained BERT models
| |book_corpus_wiki_en_uncased|book_corpus_wiki_en_cased|wiki_multilingual|wiki_multilingual_cased|wiki_cn|
|---|---|---|---|---|---|
|bert_12_768_12|✓|✓|✓|✓|✓|
|bert_24_1024_16|x|✓|x|x|x|
Example of using the large pre-trained BERT model from Google
```python
from bert_embedding import BertEmbedding
bert_embedding = BertEmbedding(model='bert_24_1024_16', dataset_name='book_corpus_wiki_en_cased')
```
Source: [gluonnlp](http://gluon-nlp.mxnet.io/model_zoo/bert/index.html)
%prep
%autosetup -n bert-embedding-1.0.1
%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-bert-embedding -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.1-1
- Package Spec generated
|
