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--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-large results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.62686567164178 - type: ap value: 34.46944126809772 - type: f1 value: 66.23684353950857 - task: type: Classification dataset: type: mteb/amazo

--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-small results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.22388059701493 - type: ap value: 36.09895941426988 - type: f1 value: 67.3205651539195 - task: type: Classification dataset: type: mteb/amazon

--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-large-zh results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC conf...

General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer different sizes of models for both Chinese and English Languages. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc. | Models | Language | Max Sequence Length | Dimension | Model Size | |:-----: | :-----: |:-----: |:-----: |:-----: | |GTE-large-zh | Chinese | 512 | 1024 | 0.67GB | |GTE-base-zh | Chinese | 512 | 1024 | 0.67GB | |GTE-small-zh | Chinese | 512 | 1024 | 0.67GB | |GTE-large | English | 512 | 1024 | 0.67GB | |GTE-base | English | 512 | 1024 | 0.67GB | |GTE-small | English | 512 | 1024 | 0.67GB | We compared the performance of the GTE models with other popular text embedding models on the MTEB (CMTEB for Chinese language) benchmark. For more detailed comparison results, please refer to the MTEB leaderboard. | Model | Model Size (GB) | Embedding Dimensions | Sequence Length | Average (35 datasets) | Classification (9 datasets) | Clustering (4 datasets) | Pair Classification (2 datasets) | Reranking (4 datasets) | Retrieval (8 datasets) | STS (8 datasets) | | ------------------- | -------------- | -------------------- | ---------------- | --------------------- | ------------------------------------ | ------------------------------ | --------------------------------------- | ------------------------------ | ---------------------------- | ------------------------ | | gte-large-zh | 0.65 | 1024 | 512 | 66.72 | 71.34 | 53.07 | 81.14 | 67.42 | 72.49 | 57.82 | | gte-base-zh | 0.20 | 768 | 512 | 65.92 | 71.26 | 53.86 | 80.44 | 67.00 | 71.71 | 55.96 | | stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 | | stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 | | bge-large-zh-v1.5 | 1.3 | 1024 | 512 | 64.53 | 69.13 | 48.99 | 81.6 | 65.84 | 70.46 | 56.25 | | stella-base-zh-v2 | 0.21 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.96 | 66.1 | 70.08 | 56.92 | | stella-base-zh | 0.21 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 | | piccolo-large-zh | 0.65 | 1024 | 512 | 64.11 | 67.03 | 47.04 | 78.38 | 65.98 | 70.93 | 58.02 | | piccolo-base-zh | 0.2 | 768 | 512 | 63.66 | 66.98 | 47.12 | 76.61 | 66.68 | 71.2 | 55.9 | | gte-small-zh | 0.1 | 512 | 512 | 60.08 | 64.49 | 48.95 | 69.99 | 66.21 | 65.50 | 49.72 | | bge-small-zh-v1.5 | 0.1 | 512 | 512 | 57.82 | 63.96 | 44.18 | 70.4 | 60.92 | 61.77 | 49.1 | | m3e-base | 0.41 | 768 | 512 | 57.79 | 67.52 | 47.68 | 63.99 | 59.54| 56.91 | 50.47 | |text-embedding-ada-002(openai) | - | 1536| 8192 | 53.02 | 64.31 | 45.68 | 69.56 | 54.28 | 52.0 | 43.35 | This model exclusively caters to Chinese texts, and any lengthy texts will be truncated to a maximum of 512 tokens. If you find our paper or models helpful, please consider citing them as follows:

General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer different sizes of models for both Chinese and English Languages. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc. | Models | Language | Max Sequence Length | Dimension | Model Size | |:-----: | :-----: |:-----: |:-----: |:-----: | |GTE-large-zh | Chinese | 512 | 1024 | 0.67GB | |GTE-base-zh | Chinese | 512 | 512 | 0.21GB | |GTE-small-zh | Chinese | 512 | 512 | 0.10GB | |GTE-large | English | 512 | 1024 | 0.67GB | |GTE-base | English | 512 | 512 | 0.21GB | |GTE-small | English | 512 | 384 | 0.10GB | We compared the performance of the GTE models with other popular text embedding models on the MTEB (CMTEB for Chinese language) benchmark. For more detailed comparison results, please refer to the MTEB leaderboard. | Model | Model Size (GB) | Embedding Dimensions | Sequence Length | Average (35 datasets) | Classification (9 datasets) | Clustering (4 datasets) | Pair Classification (2 datasets) | Reranking (4 datasets) | Retrieval (8 datasets) | STS (8 datasets) | | ------------------- | -------------- | -------------------- | ---------------- | --------------------- | ------------------------------------ | ------------------------------ | --------------------------------------- | ------------------------------ | ---------------------------- | ------------------------ | | gte-large-zh | 0.65 | 1024 | 512 | 66.72 | 71.34 | 53.07 | 81.14 | 67.42 | 72.49 | 57.82 | | gte-base-zh | 0.20 | 768 | 512 | 65.92 | 71.26 | 53.86 | 80.44 | 67.00 | 71.71 | 55.96 | | stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 | | stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 | | bge-large-zh-v1.5 | 1.3 | 1024 | 512 | 64.53 | 69.13 | 48.99 | 81.6 | 65.84 | 70.46 | 56.25 | | stella-base-zh-v2 | 0.21 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.96 | 66.1 | 70.08 | 56.92 | | stella-base-zh | 0.21 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 | | piccolo-large-zh | 0.65 | 1024 | 512 | 64.11 | 67.03 | 47.04 | 78.38 | 65.98 | 70.93 | 58.02 | | piccolo-base-zh | 0.2 | 768 | 512 | 63.66 | 66.98 | 47.12 | 76.61 | 66.68 | 71.2 | 55.9 | | gte-small-zh | 0.1 | 512 | 512 | 60.04 | 64.35 | 48.95 | 69.99 | 66.21 | 65.50 | 49.72 | | bge-small-zh-v1.5 | 0.1 | 512 | 512 | 57.82 | 63.96 | 44.18 | 70.4 | 60.92 | 61.77 | 49.1 | | m3e-base | 0.41 | 768 | 512 | 57.79 | 67.52 | 47.68 | 63.99 | 59.54| 56.91 | 50.47 | |text-embedding-ada-002(openai) | - | 1536| 8192 | 53.02 | 64.31 | 45.68 | 69.56 | 54.28 | 52.0 | 43.35 | This model exclusively caters to Chinese texts, and any lengthy texts will be truncated to a maximum of 512 tokens. If you find our paper or models helpful, please consider citing them as follows: