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BERT Application

Project description

BAND:BERT Application aNd Deployment

A simple and efficient BERT model training and deployment framework.

Contributors Forks Stargazers Issues MIT License


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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

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