distfuse 0.1.3

Compute DistFuse similarity scores from embedding models and APIs

DistFuse

DistFuse is a library to calculate similarity scores between two collections of text sequences encoded using transformer models. This library allows combining multiple models, including Hugging Face encoder models and embed APIs from Cohere and OpenAI. This is the same implementation of DistFuse from the MINERS paper. This library is useful for bitext mining, dense retrieval, retrieval-based classification, and retrieval-augmented generation (RAG).

Table of Contents

• Install
• Reference
• Usage
• How to Contribute?

Install

pip install distfuse

Reference

If you use any source codes included in this toolkit in your work, please cite the following papers [1] [2].

@article{winata2024miners,
  title={MINERS: Multilingual Language Models as Semantic Retrievers},
  author={Winata, Genta Indra and Zhang, Ruochen and Adelani, David Ifeoluwa},
  journal={arXiv preprint arXiv:2406.07424},
  year={2024}
}
@inproceedings{winata2023efficient,
  title={Efficient Zero-Shot Cross-lingual Inference via Retrieval},
  author={Winata, Genta and Xie, Lingjue and Radhakrishnan, Karthik and Gao, Yifan and Preo{\c{t}}iuc-Pietro, Daniel},
  booktitle={Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)},
  pages={93--104},
  year={2023}
}

Usage

We support hf (Hugging Face models), and APIs, such as cohere, and openai. For dist_measure, we support cosine, euclidean, and manhattan. If you are planning to use API models, please pass the appropriate token to openai_token or cohere_token. To use more than one model, add the model information to model_checkpoints and the weight to weights. There is no limit to the number of models you can use.

e.g., DistFuse with 2 models.

from distfuse import DistFuse

model_checkpoints = [["sentence-transformers/LaBSE", "hf"], ["sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "hf"]]
weights = [1, 1]
dist_measure = "cosine" # cosine, euclidean, manhattan
model = DistFuse(model_checkpoints, weights, dist_measure, openai_token="", cohere_token="")

scores = model.score_pairs(["I like apple", "I like cats"], ["I like orange", "I like dogs"])
print(scores)

e.g., DistFuse with 3 models.

from distfuse import DistFuse

model_checkpoints = [["sentence-transformers/LaBSE", "hf"], ["sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "hf"], ["text-embedding-3-large", "openai"]]
weights = [1, 1, 1]
dist_measure = "cosine"
model = DistFuse(model_checkpoints, weights, dist_measure, openai_token="", cohere_token="")

scores = model.score_pairs(["I like apple", "I like cats"], ["I like orange", "I like dogs"])
print(scores)

🚀 How to Contribute?

Feel free to create an issue if you have any questions. And, create a PR for fixing bugs or adding improvements.

If you are interested to create an extension of this work, feel free to reach out to us!

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