
## Multilingual-E5-base

[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024

This model has 12 layers and the embedding size is 768.

## Usage

Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.

```python
import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]


# Each input text should start with "query: " or "passage: ", even for non-English texts.
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
               'query: 南瓜的家常做法',
               "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
               "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]

tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-base')
model = AutoModel.from_pretrained('intfloat/multilingual-e5-base')

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
```

## Supported Languages

This model is initialized from [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.

## Training Details

**Initialization**: [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)

**First stage**: contrastive pre-training with weak supervision

| Dataset                                                                                                | Weak supervision                      | # of text pairs |
|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
| Filtered [mC4](https://huggingface.co/datasets/mc4)                                                    | (title, page content)                 | 1B              |
| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news)                               | (title, news content)                 | 400M            |
| [NLLB](https://huggingface.co/datasets/allenai/nllb)                                                   | translation pairs                     | 2.4B            |
| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia)                                        | (hierarchical section title, passage) | 150M            |
| Filtered [Reddit](https://www.reddit.com/)                                                             | (comment, response)                   | 800M            |
| [S2ORC](https://github.com/allenai/s2orc)                                                              | (title, abstract) and citation pairs  | 100M            |
| [Stackexchange](https://stackexchange.com/)                                                            | (question, answer)                    | 50M             |
| [xP3](https://huggingface.co/datasets/bigscience/xP3)                                                  | (input prompt, response)              | 80M             |
| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | -                                     | 10M             |

**Second stage**: supervised fine-tuning

| Dataset                                                                                | Language     | # of text pairs |
|----------------------------------------------------------------------------------------|--------------|-----------------|
| [MS MARCO](https://microsoft.github.io/msmarco/)                                       | English      | 500k            |
| [NQ](https://github.com/facebookresearch/DPR)                                          | English      | 70k             |
| [Trivia QA](https://github.com/facebookresearch/DPR)                                   | English      | 60k             |
| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE)                             | English      | <300k           |
| [ELI5](https://huggingface.co/datasets/eli5)                                           | English      | 500k            |
| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese      | 86k             |
| [KILT Fever](https://huggingface.co/datasets/kilt_tasks)                               | English      | 70k             |
| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks)                            | English      | 70k             |
| [SQuAD](https://huggingface.co/datasets/squad)                                         | English      | 87k             |
| [Quora](https://huggingface.co/datasets/quora)                                         | English      | 150k            |
| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi)                                                                           | 11 languages | 50k             |
| [MIRACL](https://huggingface.co/datasets/miracl/miracl)                                                                             | 16 languages | 40k             |

For all labeled datasets, we only use its training set for fine-tuning.

For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).

## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)

| Model                 | Avg MRR@10 |       | ar   | bn | en | fi | id | ja | ko | ru | sw   | te | th |
|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
| BM25                  | 33.3       | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
| mDPR                  | 16.7       | | 26.0 | 25.8  | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
| BM25 + mDPR           | 41.7       | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
|                       |            |
| multilingual-e5-small | 64.4       | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
| multilingual-e5-base  | 65.9       | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
| multilingual-e5-large | **70.5**   | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |

## MTEB Benchmark Evaluation

Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results 
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).

## Support for Sentence Transformers

Below is an example for usage with sentence_transformers.
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-base')
input_texts = [
    'query: how much protein should a female eat',
    'query: 南瓜的家常做法',
    "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i     s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini     ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮     ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,     放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油     锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀      6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
```

Package requirements

`pip install sentence_transformers~=2.2.2`

Contributors: [michaelfeil](https://huggingface.co/michaelfeil)

## 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, bitext mining, 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{wang2024multilingual,
  title={Multilingual E5 Text Embeddings: A Technical Report},
  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
  journal={arXiv preprint arXiv:2402.05672},
  year={2024}
}
```

## Limitations

Long texts will be truncated to at most 512 tokens.

{"language": ["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", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh"], "license": "mit", "tags": ["mteb", "Sentence Transformers", "sentence-similarity", "sentence-transformers"], "model-index": [{"name": "multilingual-e5-base", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 78.97014925373135}, {"type": "ap", "value": 43.69351129103008}, {"type": "f1", "value": 73.38075030070492}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (de)", "type": "mteb/amazon_counterfactual", "config": "de", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 71.7237687366167}, {"type": "ap", "value": 82.22089859962671}, {"type": "f1", "value": 69.95532758884401}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en-ext)", "type": "mteb/amazon_counterfactual", "config": "en-ext", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 79.65517241379312}, {"type": "ap", "value": 28.507918657094738}, {"type": "f1", "value": 66.84516013726119}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (ja)", "type": "mteb/amazon_counterfactual", "config": "ja", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 73.32976445396146}, {"type": "ap", "value": 20.720481637566014}, {"type": "f1", "value": 59.78002763416003}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": "e2d317d38cd51312af73b3d32a06d1a08b442046"}, "metrics": [{"type": "accuracy", "value": 90.63775}, {"type": "ap", "value": 87.22277903861716}, {"type": "f1", "value": 90.60378636386807}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (en)", "type": "mteb/amazon_reviews_multi", "config": "en", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 44.546}, {"type": "f1", "value": 44.05666638370923}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (de)", "type": "mteb/amazon_reviews_multi", "config": "de", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 41.828}, {"type": "f1", "value": 41.2710255644252}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (es)", "type": "mteb/amazon_reviews_multi", "config": "es", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 40.534}, {"type": "f1", "value": 39.820743174270326}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (fr)", "type": "mteb/amazon_reviews_multi", "config": "fr", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 39.684}, {"type": "f1", "value": 39.11052682815307}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (ja)", "type": "mteb/amazon_reviews_multi", "config": "ja", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 37.436}, {"type": "f1", "value": 37.07082931930871}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (zh)", "type": "mteb/amazon_reviews_multi", "config": "zh", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 37.226000000000006}, {"type": "f1", "value": 36.65372077739185}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB ArguAna", "type": "arguana", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 22.831000000000003}, {"type": "map_at_10", "value": 36.42}, {"type": "map_at_100", "value": 37.699}, {"type": "map_at_1000", "value": 37.724000000000004}, {"type": "map_at_3", "value": 32.207}, {"type": "map_at_5", "value": 34.312}, {"type": "mrr_at_1", "value": 23.257}, {"type": "mrr_at_10", "value": 36.574}, {"type": "mrr_at_100", "value": 37.854}, {"type": "mrr_at_1000", "value": 37.878}, {"type": "mrr_at_3", "value": 32.385000000000005}, {"type": "mrr_at_5", "value": 34.48}, {"type": "ndcg_at_1", "value": 22.831000000000003}, {"type": "ndcg_at_10", "value": 44.230000000000004}, {"type": "ndcg_at_100", "value": 49.974000000000004}, {"type": "ndcg_at_1000", "value": 50.522999999999996}, {"type": "ndcg_at_3", "value": 35.363}, {"type": "ndcg_at_5", "value": 39.164}, {"type": "precision_at_1", "value": 22.831000000000003}, {"type": "precision_at_10", "value": 6.935}, {"type": "precision_at_100", "value": 0.9520000000000001}, {"type": "precision_at_1000", "value": 0.099}, {"type": "precision_at_3", "value": 14.841}, {"type": "precision_at_5", "value": 10.754}, {"type": "recall_at_1", "value": 22.831000000000003}, {"type": "recall_at_10", "value": 69.346}, {"type": "recall_at_100", "value": 95.235}, {"type": "recall_at_1000", "value": 99.36}, {"type": "recall_at_3", "value": 44.523}, {"type": "recall_at_5", "value": 53.769999999999996}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ArxivClusteringP2P", "type": "mteb/arxiv-clustering-p2p", "config": "default", "split": "test", "revision": "a122ad7f3f0291bf49cc6f4d32aa80929df69d5d"}, "metrics": [{"type": "v_measure", "value": 40.27789869854063}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ArxivClusteringS2S", "type": "mteb/arxiv-clustering-s2s", "config": "default", "split": "test", "revision": "f910caf1a6075f7329cdf8c1a6135696f37dbd53"}, "metrics": [{"type": "v_measure", "value": 35.41979463347428}]}, {"task": {"type": "Reranking"}, "dataset": {"name": "MTEB AskUbuntuDupQuestions", "type": "mteb/askubuntudupquestions-reranking", "config": "default", "split": "test", "revision": "2000358ca161889fa9c082cb41daa8dcfb161a54"}, "metrics": [{"type": "map", "value": 58.22752045109304}, {"type": "mrr", "value": 71.51112430198303}]}, {"task": {"type": "STS"}, "dataset": {"name": "MTEB BIOSSES", "type": "mteb/biosses-sts", "config": "default", "split": "test", "revision": "d3fb88f8f02e40887cd149695127462bbcf29b4a"}, "metrics": [{"type": "cos_sim_pearson", "value": 84.71147646622866}, {"type": "cos_sim_spearman", "value": 85.059167046486}, {"type": "euclidean_pearson", "value": 75.88421613600647}, {"type": "euclidean_spearman", "value": 75.12821787150585}, {"type": "manhattan_pearson", "value": 75.22005646957604}, {"type": "manhattan_spearman", "value": 74.42880434453272}]}, {"task": {"type": "BitextMining"}, "dataset": {"name": "MTEB BUCC (de-en)", "type": "mteb/bucc-bitext-mining", "config": "de-en", "split": "test", "revision": "d51519689f32196a32af33b075a01d0e7c51e252"}, "metrics": [{"type": "accuracy", "value": 99.23799582463465}, {"type": "f1", "value": 99.12665274878218}, {"type": "precision", "value": 99.07098121085595}, {"type": "recall", "value": 99.23799582463465}]}, {"task": {"type": "BitextMining"}, "dataset": {"name": "MTEB BUCC (fr-en)", "type": "mteb/bucc-bitext-mining", "config": "fr-en", "split": "test", "revision": "d51519689f32196a32af33b075a01d0e7c51e252"}, "metrics": [{"type": "accuracy", "value": 97.88685890380806}, {"type": "f1", "value": 97.59336708489249}, {"type": "precision", "value": 97.44662117543473}, {"type": "recall", "value": 97.88685890380806}]}, {"task": {"type": "BitextMining"}, "dataset": {"name": "MTEB BUCC (ru-en)", "type": "mteb/bucc-bitext-mining", "config": "ru-en", "split": "test", "revision": "d51519689f32196a32af33b075a01d0e7c51e252"}, "metrics": [{"type": "accuracy", "value": 97.47142362313821}, {"type": "f1", "value": 97.1989377670015}, {"type": "precision", "value": 97.06384944001847}, {"type": "recall", "value": 97.47142362313821}]}, {"task": {"type": "BitextMining"}, "dataset": {"name": "MTEB BUCC (zh-en)", "type": "mteb/bucc-bitext-mining", "config": "zh-en", 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"mrr_at_5", "value": 39.966}, {"type": "ndcg_at_1", "value": 26.531}, {"type": "ndcg_at_10", "value": 21.353}, {"type": "ndcg_at_100", "value": 31.087999999999997}, {"type": "ndcg_at_1000", "value": 43.163000000000004}, {"type": "ndcg_at_3", "value": 22.999}, {"type": "ndcg_at_5", "value": 21.451}, {"type": "precision_at_1", "value": 28.571}, {"type": "precision_at_10", "value": 19.387999999999998}, {"type": "precision_at_100", "value": 6.265}, {"type": "precision_at_1000", "value": 1.4160000000000001}, {"type": "precision_at_3", "value": 24.490000000000002}, {"type": "precision_at_5", "value": 21.224}, {"type": "recall_at_1", "value": 2.603}, {"type": "recall_at_10", "value": 14.474}, {"type": "recall_at_100", "value": 40.287}, {"type": "recall_at_1000", "value": 76.606}, {"type": "recall_at_3", "value": 5.978}, {"type": "recall_at_5", "value": 7.819}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB ToxicConversationsClassification", "type": "mteb/toxic_conversations_50k", "config": "default", "split": "test", "revision": "d7c0de2777da35d6aae2200a62c6e0e5af397c4c"}, "metrics": [{"type": "accuracy", "value": 69.7848}, {"type": "ap", "value": 13.661023167088224}, {"type": "f1", "value": 53.61686134460943}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB TweetSentimentExtractionClassification", "type": "mteb/tweet_sentiment_extraction", "config": "default", "split": "test", "revision": "d604517c81ca91fe16a244d1248fc021f9ecee7a"}, "metrics": [{"type": "accuracy", "value": 61.28183361629882}, {"type": "f1", "value": 61.55481034919965}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB TwentyNewsgroupsClustering", "type": "mteb/twentynewsgroups-clustering", "config": "default", "split": "test", "revision": "6125ec4e24fa026cec8a478383ee943acfbd5449"}, "metrics": [{"type": "v_measure", "value": 35.972128420092396}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterSemEval2015", "type": "mteb/twittersemeval2015-pairclassification", "config": "default", "split": "test", "revision": "70970daeab8776df92f5ea462b6173c0b46fd2d1"}, "metrics": [{"type": "cos_sim_accuracy", "value": 85.59933241938367}, {"type": "cos_sim_ap", "value": 72.20760361208136}, {"type": "cos_sim_f1", "value": 66.4447731755424}, {"type": "cos_sim_precision", "value": 62.35539102267469}, {"type": "cos_sim_recall", "value": 71.10817941952506}, {"type": "dot_accuracy", "value": 78.98313166835548}, {"type": "dot_ap", "value": 44.492521645493795}, {"type": "dot_f1", "value": 45.814889336016094}, {"type": "dot_precision", "value": 37.02439024390244}, {"type": "dot_recall", "value": 60.07915567282321}, {"type": "euclidean_accuracy", "value": 85.3907134767837}, {"type": "euclidean_ap", "value": 71.53847289080343}, {"type": "euclidean_f1", "value": 65.95952206778834}, {"type": "euclidean_precision", "value": 61.31006346328196}, {"type": "euclidean_recall", "value": 71.37203166226914}, {"type": "manhattan_accuracy", "value": 85.40859510043511}, {"type": "manhattan_ap", "value": 71.49664104395515}, {"type": "manhattan_f1", "value": 65.98569969356485}, {"type": "manhattan_precision", "value": 63.928748144482924}, {"type": "manhattan_recall", "value": 68.17941952506597}, {"type": "max_accuracy", "value": 85.59933241938367}, {"type": "max_ap", "value": 72.20760361208136}, {"type": "max_f1", "value": 66.4447731755424}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterURLCorpus", "type": "mteb/twitterurlcorpus-pairclassification", "config": "default", "split": "test", "revision": "8b6510b0b1fa4e4c4f879467980e9be563ec1cdf"}, "metrics": [{"type": "cos_sim_accuracy", "value": 88.83261536073273}, {"type": "cos_sim_ap", "value": 85.48178133644264}, {"type": "cos_sim_f1", "value": 77.87816307403935}, {"type": "cos_sim_precision", "value": 75.88953021114926}, {"type": "cos_sim_recall", "value": 79.97382198952879}, {"type": "dot_accuracy", "value": 79.76287499514883}, {"type": "dot_ap", "value": 59.17438838475084}, {"type": "dot_f1", "value": 56.34566667855996}, {"type": "dot_precision", "value": 52.50349092359864}, {"type": "dot_recall", "value": 60.794579611949494}, {"type": "euclidean_accuracy", "value": 88.76857996662397}, {"type": "euclidean_ap", "value": 85.22764834359887}, {"type": "euclidean_f1", "value": 77.65379751543554}, {"type": "euclidean_precision", "value": 75.11152683839401}, {"type": "euclidean_recall", "value": 80.37419156144134}, {"type": "manhattan_accuracy", "value": 88.6987231730508}, {"type": "manhattan_ap", "value": 85.18907981724007}, {"type": "manhattan_f1", "value": 77.51967028849757}, {"type": "manhattan_precision", "value": 75.49992701795358}, {"type": "manhattan_recall", "value": 79.65044656606098}, {"type": "max_accuracy", "value": 88.83261536073273}, {"type": "max_ap", "value": 85.48178133644264}, {"type": "max_f1", "value": 77.87816307403935}]}]}]}
sentence-transformers pytorch onnx safetensors xlm-roberta mteb Sentence Transformers sentence-similarity 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 jv ka kk km kn ko ku ky la lo lt lv mg mk ml mn mr ms my ne nl no om or pa pl ps pt ro ru sa sd si sk sl so sq sr su sv sw ta te th tl tr ug uk ur uz vi xh yi zh arxiv:2402.05672 arxiv:2108.08787 arxiv:2104.08663 arxiv:2210.07316 license:mit model-index endpoints_compatible has_space region:us
TAGS
#sentence-transformers #pytorch #onnx #safetensors #xlm-roberta #mteb #Sentence Transformers #sentence-similarity #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 #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #pa #pl #ps #pt #ro #ru #sa #sd #si #sk #sl #so #sq #sr #su #sv #sw #ta #te #th #tl #tr #ug #uk #ur #uz #vi #xh #yi #zh #arxiv-2402.05672 #arxiv-2108.08787 #arxiv-2104.08663 #arxiv-2210.07316 #license-mit #model-index #endpoints_compatible #has_space #region-us 

Multilingual-E5-base
--------------------


Multilingual E5 Text Embeddings: A Technical Report.
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024


This model has 12 layers and the embedding size is 768.


Usage
-----


Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.


Supported Languages
-------------------


This model is initialized from xlm-roberta-base
and continually trained on a mixture of multilingual datasets.
It supports 100 languages from xlm-roberta,
but low-resource languages may see performance degradation.


Training Details
----------------


Initialization: xlm-roberta-base


First stage: contrastive pre-training with weak supervision


Dataset: Filtered mC4, Weak supervision: (title, page content), # of text pairs: 1B
Dataset: CC News, Weak supervision: (title, news content), # of text pairs: 400M
Dataset: NLLB, Weak supervision: translation pairs, # of text pairs: 2.4B
Dataset: Wikipedia, Weak supervision: (hierarchical section title, passage), # of text pairs: 150M
Dataset: Filtered Reddit, Weak supervision: (comment, response), # of text pairs: 800M
Dataset: S2ORC, Weak supervision: (title, abstract) and citation pairs, # of text pairs: 100M
Dataset: Stackexchange, Weak supervision: (question, answer), # of text pairs: 50M
Dataset: xP3, Weak supervision: (input prompt, response), # of text pairs: 80M
Dataset: Miscellaneous unsupervised SBERT data, Weak supervision: -, # of text pairs: 10M


Second stage: supervised fine-tuning


Dataset: MS MARCO, Language: English, # of text pairs: 500k
Dataset: NQ, Language: English, # of text pairs: 70k
Dataset: Trivia QA, Language: English, # of text pairs: 60k
Dataset: NLI from SimCSE, Language: English, # of text pairs: <300k
Dataset: ELI5, Language: English, # of text pairs: 500k
Dataset: DuReader Retrieval, Language: Chinese, # of text pairs: 86k
Dataset: KILT Fever, Language: English, # of text pairs: 70k
Dataset: KILT HotpotQA, Language: English, # of text pairs: 70k
Dataset: SQuAD, Language: English, # of text pairs: 87k
Dataset: Quora, Language: English, # of text pairs: 150k
Dataset: Mr. TyDi, Language: 11 languages, # of text pairs: 50k
Dataset: MIRACL, Language: 16 languages, # of text pairs: 40k


For all labeled datasets, we only use its training set for fine-tuning.


For other training details, please refer to our paper at URL


Benchmark Results on Mr. TyDi
-----------------------------



MTEB Benchmark Evaluation
-------------------------


Check out unilm/e5 to reproduce evaluation results
on the BEIR and MTEB benchmark.


Support for Sentence Transformers
---------------------------------


Below is an example for usage with sentence\_transformers.


Package requirements


'pip install sentence\_transformers~=2.2.2'


Contributors: michaelfeil


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, bitext mining, 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.


If you find our paper or models helpful, please consider cite as follows:


Limitations
-----------


Long texts will be truncated to at most 512 tokens.



# of text pairs: 1B
Dataset: CC News, Weak supervision: (title, news content), # of text pairs: 400M
Dataset: NLLB, Weak supervision: translation pairs, # of text pairs: 2.4B
Dataset: Wikipedia, Weak supervision: (hierarchical section title, passage), # of text pairs: 150M
Dataset: Filtered Reddit, Weak supervision: (comment, response), # of text pairs: 800M
Dataset: S2ORC, Weak supervision: (title, abstract) and citation pairs, # of text pairs: 100M
Dataset: Stackexchange, Weak supervision: (question, answer), # of text pairs: 50M
Dataset: xP3, Weak supervision: (input prompt, response), # of text pairs: 80M
Dataset: Miscellaneous unsupervised SBERT data, Weak supervision: -, # of text pairs: 10M


Second stage: supervised fine-tuning


Dataset: MS MARCO, Language: English, # of text pairs: 500k
Dataset: NQ, Language: English, # of text pairs: 70k
Dataset: Trivia QA, Language: English, # of text pairs: 60k
Dataset: NLI from SimCSE, Language: English, # of text pairs: <300k
Dataset: ELI5, Language: English, # of text pairs: 500k
Dataset: DuReader Retrieval, Language: Chinese, # of text pairs: 86k
Dataset: KILT Fever, Language: English, # of text pairs: 70k
Dataset: KILT HotpotQA, Language: English, # of text pairs: 70k
Dataset: SQuAD, Language: English, # of text pairs: 87k
Dataset: Quora, Language: English, # of text pairs: 150k
Dataset: Mr. TyDi, Language: 11 languages, # of text pairs: 50k
Dataset: MIRACL, Language: 16 languages, # of text pairs: 40k


For all labeled datasets, we only use its training set for fine-tuning.


For other training details, please refer to our paper at URL


Benchmark Results on Mr. TyDi
-----------------------------



MTEB Benchmark Evaluation
-------------------------


Check out unilm/e5 to reproduce evaluation results
on the BEIR and MTEB benchmark.


Support for Sentence Transformers
---------------------------------


Below is an example for usage with sentence\_transformers.


Package requirements


'pip install sentence\_transformers~=2.2.2'


Contributors: michaelfeil


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, bitext mining, 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.


If you find our paper or models helpful, please consider cite as follows:


Limitations
-----------


Long texts will be truncated to at most 512 tokens.
TAGS
#sentence-transformers #pytorch #onnx #safetensors #xlm-roberta #mteb #Sentence Transformers #sentence-similarity #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 #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #pa #pl #ps #pt #ro #ru #sa #sd #si #sk #sl #so #sq #sr #su #sv #sw #ta #te #th #tl #tr #ug #uk #ur #uz #vi #xh #yi #zh #arxiv-2402.05672 #arxiv-2108.08787 #arxiv-2104.08663 #arxiv-2210.07316 #license-mit #model-index #endpoints_compatible #has_space #region-us 
 # of text pairs: 1B
Dataset: CC News, Weak supervision: (title, news content), # of text pairs: 400M
Dataset: NLLB, Weak supervision: translation pairs, # of text pairs: 2.4B
Dataset: Wikipedia, Weak supervision: (hierarchical section title, passage), # of text pairs: 150M
Dataset: Filtered Reddit, Weak supervision: (comment, response), # of text pairs: 800M
Dataset: S2ORC, Weak supervision: (title, abstract) and citation pairs, # of text pairs: 100M
Dataset: Stackexchange, Weak supervision: (question, answer), # of text pairs: 50M
Dataset: xP3, Weak supervision: (input prompt, response), # of text pairs: 80M
Dataset: Miscellaneous unsupervised SBERT data, Weak supervision: -, # of text pairs: 10M


Second stage: supervised fine-tuning


Dataset: MS MARCO, Language: English, # of text pairs: 500k
Dataset: NQ, Language: English, # of text pairs: 70k
Dataset: Trivia QA, Language: English, # of text pairs: 60k
Dataset: NLI from SimCSE, Language: English, # of text pairs: <300k
Dataset: ELI5, Language: English, # of text pairs: 500k
Dataset: DuReader Retrieval, Language: Chinese, # of text pairs: 86k
Dataset: KILT Fever, Language: English, # of text pairs: 70k
Dataset: KILT HotpotQA, Language: English, # of text pairs: 70k
Dataset: SQuAD, Language: English, # of text pairs: 87k
Dataset: Quora, Language: English, # of text pairs: 150k
Dataset: Mr. TyDi, Language: 11 languages, # of text pairs: 50k
Dataset: MIRACL, Language: 16 languages, # of text pairs: 40k


For all labeled datasets, we only use its training set for fine-tuning.


For other training details, please refer to our paper at URL


Benchmark Results on Mr. TyDi
-----------------------------



MTEB Benchmark Evaluation
-------------------------


Check out unilm/e5 to reproduce evaluation results
on the BEIR and MTEB benchmark.


Support for Sentence Transformers
---------------------------------


Below is an example for usage with sentence\_transformers.


Package requirements


'pip install sentence\_transformers~=2.2.2'


Contributors: michaelfeil


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, bitext mining, 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.


If you find our paper or models helpful, please consider cite as follows:


Limitations
-----------


Long texts will be truncated to at most 512 tokens.