LiyuanLucasLiu/LD-Net
Language Model Pruning for Sequence Labeling
This project helps machine learning engineers and data scientists create highly efficient sequence labeling models, particularly for tasks like Named Entity Recognition (NER) and Chunking. It takes raw text data and generates models that can identify and categorize specific entities or phrases within that text. The key benefit is building these models to be much faster in production without sacrificing accuracy.
147 stars. No commits in the last 6 months.
Use this if you need to deploy fast and accurate Named Entity Recognition or text chunking models in applications where computational efficiency is critical.
Not ideal if you are looking for a complete, end-to-end natural language processing platform rather than a specialized tool for model efficiency.
Stars
147
Forks
13
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 29, 2020
Commits (30d)
0
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