Bert-Multi-Label-Text-Classification

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Bert-Multi-Label-Text-Classification is an Open Source & Models tool that provides a PyTorch implementation of pretrained BERT and XLNET models. It is designed for multi-label text classification tasks.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Bert-Multi-Label-Text-Classification?

Bert-Multi-Label-Text-Classification offers a PyTorch implementation of pretrained BERT and XLNET models specifically tailored for multi-label text classification. This open-source repository includes a structured codebase with modules for callbacks, configuration, dataset handling, model architecture, output management, text preprocessing, and training. Developers can fine-tune BERT models, preprocess data, and predict new data using provided scripts. The tool supports various dependencies like PyTorch, transformers, and scikit-learn, making it a robust solution for NLP tasks requiring multi-label classification.

Best used for

Ideal for developers and data scientists who need to implement multi-label text classification using state-of-the-art BERT and XLNET models. Especially valuable for academic research, building custom NLP applications, and fine-tuning models for specific datasets.

Common actions

classify text

fine-tune BERT

implement NLP models

process text data

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Capabilities

Key features

PyTorch BERT/XLNET implementation
Multi-label text classification
Data preprocessing scripts
Model fine-tuning
Configurable parameters

Target Audience

developerdata scientiststudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What pretrained models does this repository support?

This repository supports the PyTorch implementation of both pretrained BERT and XLNET models. It provides the necessary code and structure to utilize these powerful transformer models for multi-label text classification tasks.

What are the key dependencies for running this project?

The main dependencies include PyTorch 1.1+, transformers (version 2.5.1), csv, tqdm, numpy, pickle, scikit-learn, matplotlib, and pandas. These libraries are essential for data handling, model training, and evaluation.

How do I prepare my data for use with this tool?

You need to download your dataset, such as from Kaggle, and place it in the `pybert/dataset` directory. You can then modify `io.task_data.py` to adapt the data to your specific format and update configuration information in `pybert/configs/basic_config.py`.

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Also listed in

This tool also appears in

Research & Education › Scientific Computing

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