intfloat/e5-large
sentence-similarity model by intfloat
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Model card & documentation
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News (May 2023): please switch to e5-large-v2, which has better performance and same method of usage. 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 24 layers and the embedding size is 1024. Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf. Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark. Below is an example for usage with sentencetransformers. Package requirements pip install sentencetransformers=2.2.2 Contributors: michaelfeil 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: 2. Why are my reproduced results slightly different from reported in the…
Read the full model card ↗ · Preview checked 2026-09-24T06:44:33.441Z
Inside the original model card — Document outline
- E5-large
- Usage
- Training Details
- Benchmark Evaluation
- Support for Sentence Transformers
- FAQ
- Citation
- Limitations
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Model overview
intfloat/e5-large is a sentence-similarity model repository published by intfloat on Hugging Face. The source reports the sentence-transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- sentence-similarity
- Library
- sentence-transformers
- Recent downloads (30 days)
- 131,057
- Cumulative likes
- 83
- Hugging Face trending score
- 1
- Reported safetensors parameters
- 335,142,400
- Architecture
- BertModel
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2022-12-26T06:03:12.000Z
- Last modified
- 2023-08-07T04:59:49.000Z
Compatibility and lineage
Reported languages: en
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-24T06:38:33.695Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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