BAND：BERT Application aNd Deployment

A simple and efficient BERT model training and deployment framework.

BAND

BAND：BERT Application aNd Deployment
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What is it

Encoding/Embedding is a upstream task of encoding any inputs in the form of text, image, audio, video, transactional data to fixed length vector. Embeddings are quite popular in the field of NLP, there has been various Embeddings models being proposed in recent years by researchers, some of the famous one are bert, xlnet, word2vec etc. The goal of this repo is to build one stop solution for all embeddings techniques available, here we are starting with popular text embeddings for now and later on we aim to add as much technique for image, audio, video inputs also.
Finally, embedding-as-service help you to encode any given text to fixed length vector from supported embeddings and models.

💾 Installation

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Install the band via pip.

$ pip install band -U

Note that the code MUST be running on Python >= 3.6. Again module does not support Python 2!

⚡ ️Getting Started

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Dataset

For more information about dataset, see

Dataset Name	Language	TASK	Description
ChnSentiCorp	CN	Text Classification	Binary Classification
LCQMC	CN	Question Answer Match	Binary Classification
MSRA_NER	CN	Named Entity Recognition	Sequence Labeling
Toxic	EN	Text Classification	Multi-label Multi-label
Thucnews	CN	Text Classification	Multi-class Classification
SQUAD	EN	Machine Reading Comprehension	Span
DRCD	CN	Machine Reading Comprehension	Span
CMRC	CN	Machine Reading Comprehension	Span
GLUE	EN

Current Pretrained Models

For more information about pretrained models, see

Metadata

Release files for band 0.3.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for band 0.3.3
File	Size	Uploaded
band-0.3.3.tar.gz	33.1 kB	Dec 2, 2019	Details

Built distribution (wheel)

Table of built distributions (wheels) for band 0.3.3
File	Interpreter	ABI	Platform	Reset
band-0.3.3-py3-none-any.whl 42.4 kB Dec 2, 2019	Python 3	none	any	Details

Total release size: 75.5 kB

Release files / band-0.3.3.tar.gz

Download URL	band-0.3.3.tar.gz
Size	33.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`2c29b458ce29e680f18bf7fed38b33e069b3748cbfdd794542ff1e5a5639bd68`
BLAKE2b-256 checksum How to use checksums	`779088904e7eb73fa42d8e84a7d19b261bb8b2edb20a8b11933f05d5a34cd244`
Upload date	Dec 2, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/2.0.0 pkginfo/1.4.2 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.31.0 CPython/3.7.0`

Release files / band-0.3.3-py3-none-any.whl

Download URL	band-0.3.3-py3-none-any.whl
Size	42.4 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`f34663486995cfe9e8c8cd65a8bdb82b3a05f6e260fe290750f27e82f2215375`
BLAKE2b-256 checksum How to use checksums	`3de0448383d7ec49e9d6971730dd096ab6160948a599bf69cc01cd89b687dea7`
Upload date	Dec 2, 2019
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/2.0.0 pkginfo/1.4.2 requests/2.22.0 setuptools/41.6.0 requests-toolbelt/0.9.1 tqdm/4.31.0 CPython/3.7.0`