intfloat/multilingual-e5-large-instruct
feature-extraction model by intfloat
From the publisher
Model card & documentation
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Multilingual E5 Text Embeddings: A Technical Report. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 24 layers and the embedding size is 1024. Below are examples to encode queries and passages from the MS-MARCO passage ranking dataset. Usage with Infinity: This model is initialized from xlm-roberta-large and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. Initialization: xlm-roberta-large First stage: contrastive pre-training with 1 billion weakly supervised text pairs. Second stage: fine-tuning on datasets from the E5-mistral paper. Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark. 1. Do I need to add instructions to the query? Yes, this is how the model is trained, otherwise you will see a performance degradation. The task definition should be a one-sentence instruction that describes the task. This…
Read the full model card ↗ · Preview checked 2026-10-29T16:53:32.342Z
Inside the original model card — Document outline
- Multilingual-E5-large-instruct
- Usage
- Transformers
- Sentence Transformers
- Infinity
- Supported Languages
- Training Details
- MTEB Benchmark Evaluation
- FAQ
- Citation
- Limitations
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Documentation belongs to its respective authors. Reported project/model license: mit. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
intfloat/multilingual-e5-large-instruct is a feature-extraction 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
- feature-extraction
- Library
- sentence-transformers
- Recent downloads (30 days)
- 1,820,825
- Cumulative likes
- 656
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 559,890,432
- Architecture
- XLMRobertaModel
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2024-02-08T12:59:32.000Z
- Last modified
- 2025-07-10T10:12:37.000Z
Compatibility and lineage
Reported languages: multilingual, af, am, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, el, en, eo, es, et, eu, fa, fi, fr, fy, ga, gd, gl, gu, ha, he, hi, hr, hu, hy, id, is, it, ja
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 2026-11-01T06:54:29.511Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.