--- tags: - mteb - sentence-transformers - transformers - multilingual - sentence-similarity license: apache-2.0 language: - af - ar - az - be - bg - bn - ca - ceb - cs - cy - da - de - el - en - es - et - eu - fa - fi - fr - gl - gu - he - hi - hr - ht - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ky - lo - lt - lv - mk - ml - mn - mr - ms - my - ne - nl - 'no' - pa - pl - pt - qu - ro - ru - si - sk - sl - so - sq - sr - sv - sw - ta - te - th - tl - tr - uk - ur - vi - yo - zh model-index: - name: gte-multilingual-base (dense) results: - task: type: Clustering dataset: type: PL-MTEB/8tags-clustering name: MTEB 8TagsClustering config: default split: test revision: None metrics: - type: v_measure value: 33.66681726329994 - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: b44c3b011063adb25877c13823db83bb193913c4 metrics: - type: cos_sim_spearman value: 43.54760696384009 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 metrics: - type: cos_sim_spearman value: 48.91186363417501 - task: type: Classification dataset: type: PL-MTEB/allegro-reviews name: MTEB AllegroReviews config: default split: test revision: None metrics: - type: accuracy value: 41.689860834990064 - task: type: Clustering dataset: type: lyon-nlp/alloprof name: MTEB AlloProfClusteringP2P config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics: - type: v_measure value: 54.20241337977897 - task: type: Clustering dataset: type: lyon-nlp/alloprof name: MTEB AlloProfClusteringS2S config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics: - type: v_measure value: 44.34083695608643 - task: type: Reranking dataset: type: lyon-nlp/mteb-fr-reranking-alloprof-s2p name: MTEB AlloprofReranking config: default split: test revision: 666fdacebe0291776e86f29345663dfaf80a0db9 metrics: - type: map value: 64.91495250072002 - task: type: Retrieval dataset: type: lyon-nlp/alloprof name: MTEB AlloprofRetrieval config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics: - type: ndcg_at_10 value: 53.638 - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.95522388059702 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 80.717625 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.64199999999999 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (de) config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.108 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (es) config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - 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type: cos_sim_ap value: 74.90132799162956 - task: type: STS dataset: type: PL-MTEB/cdscr-sts name: MTEB CDSC-R config: default split: test revision: None metrics: - type: cos_sim_spearman value: 90.30727955142524 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 metrics: - type: v_measure value: 37.94850105022274 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f metrics: - type: v_measure value: 38.11958675421534 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: 8d7f1e942507dac42dc58017c1a001c3717da7df metrics: - type: map value: 86.10950950485399 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: 23d186750531a14a0357ca22cd92d712fd512ea0 metrics: - 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**High Performance**: Achieves state-of-the-art (SOTA) results in multilingual retrieval tasks and multi-task representation model evaluations when compared to models of similar size. - **Training Architecture**: Trained using an encoder-only transformers architecture, resulting in a smaller model size. Unlike previous models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this model has lower hardware requirements for inference, offering a 10x increase in inference speed. - **Long Context**: Supports text lengths up to **8192** tokens. - **Multilingual Capability**: Supports over **70** languages. - **Elastic Dense Embedding**: Support elastic output dense representation while maintaining the effectiveness of downstream tasks, which significantly reduces storage costs and improves execution efficiency. - **Sparse Vectors**: In addition to dense representations, it can also generate sparse vectors. **Paper**: [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669) ## Model Information - Model Size: 305M - Embedding Dimension: 768 - Max Input Tokens: 8192 ## Usage - **It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).** - **How to use it offline: [new-impl/discussions/2](https://huggingface.co/Alibaba-NLP/new-impl/discussions/2#662b08d04d8c3d0a09c88fa3)** - **How to use with [TEI](https://github.com/huggingface/text-embeddings-inference): [refs/pr/7](https://huggingface.co/Alibaba-NLP/gte-multilingual-base/discussions/7#66bfb82ea03b764ca92a2221)** ### Get Dense Embeddings with Transformers ```python # Requires transformers>=4.36.0 import torch.nn.functional as F from transformers import AutoModel, AutoTokenizer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "北京", "快排算法介绍" ] model_name_or_path = 'Alibaba-NLP/gte-multilingual-base' tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True) # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) dimension=768 # The output dimension of the output embedding, should be in [128, 768] embeddings = outputs.last_hidden_state[:, 0][:dimension] embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) # [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]] ``` ### Use with sentence-transformers ```python # Requires sentence-transformers>=3.0.0 from sentence_transformers import SentenceTransformer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "北京", "快排算法介绍" ] model_name_or_path="Alibaba-NLP/gte-multilingual-base" model = SentenceTransformer(model_name_or_path, trust_remote_code=True) embeddings = model.encode(input_texts, normalize_embeddings=True) # embeddings.shape (4, 768) # sim scores scores = model.similarity(embeddings[:1], embeddings[1:]) print(scores.tolist()) # [[0.301699697971344, 0.7503870129585266, 0.32030850648880005]] ``` ### Use with infinity Usage via docker and [infinity](https://github.com/michaelfeil/infinity), MIT Licensed. ``` docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \ michaelf34/infinity:0.0.69 \ v2 --model-id Alibaba-NLP/gte-multilingual-base --revision "main" --dtype float16 --batch-size 32 --device cuda --engine torch --port 7997 ``` ### Use with custom code to get dense embeddigns and sparse token weights ```python # You can find the script gte_embedding.py in https://huggingface.co/Alibaba-NLP/gte-multilingual-base/blob/main/scripts/gte_embedding.py from gte_embedding import GTEEmbeddidng model_name_or_path = 'Alibaba-NLP/gte-multilingual-base' model = GTEEmbeddidng(model_name_or_path) query = "中国的首都在哪儿" docs = [ "what is the capital of China?", "how to implement quick sort in python?", "北京", "快排算法介绍" ] embs = model.encode(docs, return_dense=True,return_sparse=True) print('dense_embeddings vecs', embs['dense_embeddings']) print('token_weights', embs['token_weights']) pairs = [(query, doc) for doc in docs] dense_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.0) sparse_scores = model.compute_scores(pairs, dense_weight=0.0, sparse_weight=1.0) hybrid_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.3) print('dense_scores', dense_scores) print('sparse_scores', sparse_scores) print('hybrid_scores', hybrid_scores) # dense_scores [0.85302734375, 0.257568359375, 0.76953125, 0.325439453125] # sparse_scores [0.0, 0.0, 4.600879669189453, 1.570279598236084] # hybrid_scores [0.85302734375, 0.257568359375, 2.1497951507568356, 0.7965233325958252] ``` ## Evaluation We validated the performance of the **gte-multilingual-base** model on multiple downstream tasks, including multilingual retrieval, cross-lingual retrieval, long text retrieval, and general text representation evaluation on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard), among others. ### Retrieval Task Retrieval results on [MIRACL](https://arxiv.org/abs/2210.09984) and [MLDR](https://arxiv.org/abs/2402.03216) (multilingual), [MKQA](https://arxiv.org/abs/2007.15207) (crosslingual), [BEIR](https://arxiv.org/abs/2104.08663) and [LoCo](https://arxiv.org/abs/2402.07440) (English). ![image](./images/mgte-retrieval.png) - Detail results on [MLDR](https://arxiv.org/abs/2402.03216) ![image](./images/mgte-retrieval.png) - Detail results on [LoCo](https://arxiv.org/abs/2402.07440) ### MTEB Results on MTEB English, Chinese, French, Polish ![image](./images/mgte-mteb.png) **More detailed experimental results can be found in the [paper](https://arxiv.org/pdf/2407.19669)**. ## Cloud API Services In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) series models, GTE series models are also available as commercial API services on Alibaba Cloud. - [Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-embedding/): Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service. - [ReRank Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-sorting-model/): The gte-rerank model service is available. Note that the models behind the commercial APIs are not entirely identical to the open-source models. ## Citation If you find our paper or models helpful, please consider cite: ``` @inproceedings{zhang2024mgte, title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others}, booktitle={Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track}, pages={1393--1412}, year={2024} } ```