T-NER is a Python library for Named Entity Recognition (NER) using transformer-based language models. It offers an easy-to-use interface for fine-tuning models, evaluating them across diverse datasets, and deploying them via a web application. The library is suitable for researchers and practitioners looking to streamline NER tasks and explore model generalization.

How It Works

T-NER leverages the PyTorch framework and integrates seamlessly with Hugging Face's Transformers library. It provides a unified API for accessing and processing numerous public NER datasets, as well as custom datasets formatted in the CoNLL IOB format. The library supports a two-stage parameter search for fine-tuning, optimizing configurations like learning rate, batch size, and CRF layer usage to identify high-performing models.

Quick Start & Requirements

• Install via pip: pip install tner
• For web app: pip install tner[app]
• Requires Python. GPU and CUDA are recommended for fine-tuning.
• Official Docs: https://github.com/asahi417/tner
• HuggingFace Group: https://huggingface.co/tner
• Online Demo: https://huggingface.co/spaces/asahi417/tner

Highlighted Details

• Supports fine-tuning with a robust parameter search strategy.
• Integrates over 100 pre-trained NER models and datasets from Hugging Face.
• Offers a web API for interactive model prediction and visualization.
• Includes functionality for cross-domain and multilingual NER evaluation.

Maintenance & Community

The project is associated with EACL 2021 and AACL 2022 publications. Further details and community interaction can be found via the GitHub repository.

Licensing & Compatibility

The library is released under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

While T-NER simplifies many aspects of NER, cross-domain generalization remains a challenge, even with large pre-trained models. The fine-tuning process, especially with extensive parameter search, can be computationally intensive.