Model details
This repository contains the files used on intfloat/e5-small-v2 converted to GGML to be used on the bert.cpp backend.
- Text Embeddings by Weakly-Supervised Contrastive Pre-training.
- Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
- This model has 12 layers and the embedding size is 384.
FAQ
1. Do I need to add the prefix "query: " and "passage: " to input texts?
Yes, this is how the model is trained, otherwise you will see a performance degradation.
Here are some rules of thumb:
Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
2. Why are my reproduced results slightly different from reported in the model card?
Different versions of transformers and pytorch could cause negligible but non-zero performance differences.
3. Why does the cosine similarity scores distribute around 0.7 to 1.0?
This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
For text embedding tasks like text retrieval or semantic similarity, what matters is the relative order of the scores instead of the absolute values, so this should not be an issue.
Citation
If you find our paper or models helpful, please consider cite as follows:
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
Limitations
This model only works for English texts. Long texts will be truncated to at most 512 tokens.