huggingface/hmtl

Health v2 · maintenance only

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1195 stars · 146 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

HMTL is a Hierarchical Multi-Task Learning model for NLP that jointly trains Named Entity Recognition, Entity Mention Detection, Relation Extraction, and Coreference Resolution using PyTorch and AllenNLP. It includes code for training, fine-tuning, and evaluating the model, plus pre-trained weights and an online demo.

Use cases

• train a multi-task NLP model for NER and relation extraction
• run named entity recognition with pre-trained weights
• learn hierarchical multi-task learning in PyTorch
• evaluate sentence embeddings from semantic tasks with SentEval
• build custom multi-task models on AllenNLP
• reproduce AAAI 2019 paper results

When to choose

• you want a state-of-the-art multi-task model for NER, entity mention detection, relation extraction, or coreference
• you're researching multi-task learning architectures in PyTorch/AllenNLP
• you need pre-trained semantic task models with a demo

When to avoid

• you need a maintained production NLP library (AllenNLP is deprecated and the repo is in maintenance)
• you want transformer-based models like those in Hugging Face Transformers
• you need tasks beyond the four supported semantic tasks

Facets

library · maturity maintenance

machine-learning nlp machine-learning deep-learning python multi-task-learning pytorch allennlp named-entity-recognition relation-extraction coreference-resolution research natural-language-processing

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