%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

[![Build Status](https://travis-ci.org/imgarylai/bert-embedding.svg?branch=master)](https://travis-ci.org/imgarylai/bert-embedding) [![codecov](https://codecov.io/gh/imgarylai/bert-embedding/branch/master/graph/badge.svg)](https://codecov.io/gh/imgarylai/bert-embedding) [![PyPI version](https://badge.fury.io/py/bert-embedding.svg)](https://pypi.org/project/bert-embedding/) [![Documentation Status](https://readthedocs.org/projects/bert-embedding/badge/?version=latest)](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

[![Build Status](https://travis-ci.org/imgarylai/bert-embedding.svg?branch=master)](https://travis-ci.org/imgarylai/bert-embedding) [![codecov](https://codecov.io/gh/imgarylai/bert-embedding/branch/master/graph/badge.svg)](https://codecov.io/gh/imgarylai/bert-embedding) [![PyPI version](https://badge.fury.io/py/bert-embedding.svg)](https://pypi.org/project/bert-embedding/) [![Documentation Status](https://readthedocs.org/projects/bert-embedding/badge/?version=latest)](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

[![Build Status](https://travis-ci.org/imgarylai/bert-embedding.svg?branch=master)](https://travis-ci.org/imgarylai/bert-embedding) [![codecov](https://codecov.io/gh/imgarylai/bert-embedding/branch/master/graph/badge.svg)](https://codecov.io/gh/imgarylai/bert-embedding) [![PyPI version](https://badge.fury.io/py/bert-embedding.svg)](https://pypi.org/project/bert-embedding/) [![Documentation Status](https://readthedocs.org/projects/bert-embedding/badge/?version=latest)](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