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BERT Application

Project description

BAND:BERT Application aNd Deployment

A simple and efficient BERT model training and deployment framework.

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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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