JohnGiorgi/DeCLUTR
The corresponding code from our paper "DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations". Do not hesitate to open an issue if you run into any trouble!
This project helps machine learning engineers or data scientists create high-quality, general-purpose text embeddings without needing manually labeled data. You provide a large collection of unlabeled documents, and it processes them to output numerical representations (embeddings) that capture the meaning of your text. These embeddings can then be used for tasks like semantic search, clustering, or text similarity.
378 stars. No commits in the last 6 months.
Use this if you need powerful, unlabeled text embeddings for downstream natural language processing tasks and want to train your own models from scratch or fine-tune existing ones.
Not ideal if you already have labeled data for your specific task or if you only need pre-trained embeddings without custom training.
Stars
378
Forks
33
Language
Python
License
Apache-2.0
Category
Last pushed
Apr 21, 2023
Commits (30d)
0
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curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/JohnGiorgi/DeCLUTR"
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