diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,3856 @@ +Quantization made by Richard Erkhov. + +[Github](https://github.com/RichardErkhov) + +[Discord](https://discord.gg/pvy7H8DZMG) + +[Request more models](https://github.com/RichardErkhov/quant_request) + + +gte-Qwen2-7B-instruct - GGUF +- Model creator: https://huggingface.co/Alibaba-NLP/ +- Original model: https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/ + + +| Name | Quant method | Size | +| ---- | ---- | ---- | +| [gte-Qwen2-7B-instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q2_K.gguf) | Q2_K | 2.81GB | +| [gte-Qwen2-7B-instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ3_XS.gguf) | IQ3_XS | 3.11GB | +| [gte-Qwen2-7B-instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ3_S.gguf) | IQ3_S | 3.26GB | +| [gte-Qwen2-7B-instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K_S.gguf) | Q3_K_S | 3.25GB | +| [gte-Qwen2-7B-instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ3_M.gguf) | IQ3_M | 3.33GB | +| [gte-Qwen2-7B-instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K.gguf) | Q3_K | 3.55GB | +| [gte-Qwen2-7B-instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K_M.gguf) | Q3_K_M | 3.55GB | +| [gte-Qwen2-7B-instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q3_K_L.gguf) | Q3_K_L | 3.81GB | +| [gte-Qwen2-7B-instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ4_XS.gguf) | IQ4_XS | 3.96GB | +| [gte-Qwen2-7B-instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_0.gguf) | Q4_0 | 4.13GB | +| [gte-Qwen2-7B-instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.IQ4_NL.gguf) | IQ4_NL | 4.15GB | +| [gte-Qwen2-7B-instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_K_S.gguf) | Q4_K_S | 4.15GB | +| [gte-Qwen2-7B-instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_K.gguf) | Q4_K | 4.36GB | +| [gte-Qwen2-7B-instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_K_M.gguf) | Q4_K_M | 4.36GB | +| [gte-Qwen2-7B-instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q4_1.gguf) | Q4_1 | 4.54GB | +| [gte-Qwen2-7B-instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_0.gguf) | Q5_0 | 4.95GB | +| [gte-Qwen2-7B-instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_K_S.gguf) | Q5_K_S | 4.95GB | +| [gte-Qwen2-7B-instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_K.gguf) | Q5_K | 5.07GB | +| [gte-Qwen2-7B-instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_K_M.gguf) | Q5_K_M | 5.07GB | +| [gte-Qwen2-7B-instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q5_1.gguf) | Q5_1 | 5.36GB | +| [gte-Qwen2-7B-instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q6_K.gguf) | Q6_K | 5.82GB | +| [gte-Qwen2-7B-instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/Alibaba-NLP_-_gte-Qwen2-7B-instruct-gguf/blob/main/gte-Qwen2-7B-instruct.Q8_0.gguf) | Q8_0 | 7.54GB | + + + + +Original model description: +--- +tags: +- mteb +- sentence-transformers +- transformers +- Qwen2 +- sentence-similarity +license: apache-2.0 +model-index: +- name: gte-qwen2-7B-instruct + results: + - task: + type: Classification + dataset: + type: mteb/amazon_counterfactual + name: MTEB AmazonCounterfactualClassification (en) + config: en + split: test + revision: e8379541af4e31359cca9fbcf4b00f2671dba205 + metrics: + - type: accuracy + value: 91.31343283582089 + - type: ap + value: 67.64251402604096 + - type: f1 + value: 87.53372530755692 + - task: + type: Classification + dataset: + type: mteb/amazon_polarity + name: MTEB AmazonPolarityClassification + config: default + split: test + revision: e2d317d38cd51312af73b3d32a06d1a08b442046 + metrics: + - type: accuracy + value: 97.497825 + - type: ap + value: 96.30329547047529 + - type: f1 + value: 97.49769793778039 + - task: + type: Classification + dataset: + type: mteb/amazon_reviews_multi + name: MTEB AmazonReviewsClassification (en) + config: en + split: test + revision: 1399c76144fd37290681b995c656ef9b2e06e26d + metrics: + - type: accuracy + value: 62.564 + - type: f1 + value: 60.975777935041066 + - task: + type: Retrieval + dataset: + type: mteb/arguana + name: MTEB ArguAna + config: default + split: test + revision: c22ab2a51041ffd869aaddef7af8d8215647e41a + metrics: + - type: map_at_1 + value: 36.486000000000004 + - type: map_at_10 + value: 54.842 + - type: map_at_100 + value: 55.206999999999994 + - type: map_at_1000 + value: 55.206999999999994 + - type: map_at_3 + value: 49.893 + - type: map_at_5 + value: 53.105000000000004 + - type: mrr_at_1 + value: 37.34 + - type: mrr_at_10 + value: 55.143 + - type: mrr_at_100 + value: 55.509 + - type: mrr_at_1000 + value: 55.509 + - type: mrr_at_3 + value: 50.212999999999994 + - type: mrr_at_5 + value: 53.432 + - type: ndcg_at_1 + value: 36.486000000000004 + - type: ndcg_at_10 + value: 64.273 + - type: ndcg_at_100 + value: 65.66199999999999 + - type: ndcg_at_1000 + value: 65.66199999999999 + - type: ndcg_at_3 + value: 54.352999999999994 + - type: ndcg_at_5 + value: 60.131 + - type: precision_at_1 + value: 36.486000000000004 + - type: precision_at_10 + value: 9.395000000000001 + - type: precision_at_100 + value: 0.996 + - type: precision_at_1000 + value: 0.1 + - type: precision_at_3 + value: 22.428 + - type: precision_at_5 + value: 16.259 + - type: recall_at_1 + value: 36.486000000000004 + - type: recall_at_10 + value: 93.95400000000001 + - type: recall_at_100 + value: 99.644 + - type: recall_at_1000 + value: 99.644 + - type: recall_at_3 + value: 67.283 + - type: recall_at_5 + value: 81.294 + - task: + type: Clustering + dataset: + type: mteb/arxiv-clustering-p2p + name: MTEB ArxivClusteringP2P + config: default + split: test + revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d + metrics: + - type: v_measure + value: 56.461169803700564 + - task: + type: Clustering + dataset: + type: mteb/arxiv-clustering-s2s + name: MTEB ArxivClusteringS2S + config: default + split: test + revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 + metrics: + - type: v_measure + value: 51.73600434466286 + - task: + type: Reranking + dataset: + type: mteb/askubuntudupquestions-reranking + name: MTEB AskUbuntuDupQuestions + config: default + split: test + revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 + metrics: + - type: map + value: 67.57827065898053 + - type: mrr + value: 79.08136569493911 + - task: + type: STS + dataset: + type: mteb/biosses-sts + name: MTEB BIOSSES + config: default + split: test + revision: d3fb88f8f02e40887cd149695127462bbcf29b4a + metrics: + - type: cos_sim_pearson + value: 83.53324575999243 + - type: cos_sim_spearman + value: 81.37173362822374 + - type: euclidean_pearson + value: 82.19243335103444 + - type: euclidean_spearman + value: 81.33679307304334 + - type: manhattan_pearson + value: 82.38752665975699 + - type: manhattan_spearman + value: 81.31510583189689 + - task: + type: Classification + dataset: + type: mteb/banking77 + name: MTEB Banking77Classification + config: default + split: test + revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 + metrics: + - type: accuracy + value: 87.56818181818181 + - type: f1 + value: 87.25826722019875 + - task: + type: Clustering + dataset: + type: mteb/biorxiv-clustering-p2p + name: MTEB BiorxivClusteringP2P + config: default + split: test + revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 + metrics: + - type: v_measure + value: 50.09239610327673 + - task: + type: Clustering + dataset: + type: mteb/biorxiv-clustering-s2s + name: MTEB BiorxivClusteringS2S + config: default + split: test + revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 + metrics: + - type: v_measure + value: 46.64733054606282 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackAndroidRetrieval + config: default + split: test + revision: f46a197baaae43b4f621051089b82a364682dfeb + metrics: + - type: map_at_1 + value: 33.997 + - type: map_at_10 + value: 48.176 + - type: map_at_100 + value: 49.82 + - type: map_at_1000 + value: 49.924 + - type: map_at_3 + value: 43.626 + - type: map_at_5 + value: 46.275 + - type: mrr_at_1 + value: 42.059999999999995 + - type: mrr_at_10 + value: 53.726 + - type: mrr_at_100 + value: 54.398 + - type: mrr_at_1000 + value: 54.416 + - type: mrr_at_3 + value: 50.714999999999996 + - type: mrr_at_5 + value: 52.639 + - type: ndcg_at_1 + value: 42.059999999999995 + - type: ndcg_at_10 + value: 55.574999999999996 + - type: ndcg_at_100 + value: 60.744 + - type: ndcg_at_1000 + value: 61.85699999999999 + - type: ndcg_at_3 + value: 49.363 + - type: ndcg_at_5 + value: 52.44 + - type: precision_at_1 + value: 42.059999999999995 + - type: precision_at_10 + value: 11.101999999999999 + - type: precision_at_100 + value: 1.73 + - type: precision_at_1000 + value: 0.218 + - type: precision_at_3 + value: 24.464 + - type: precision_at_5 + value: 18.026 + - type: recall_at_1 + value: 33.997 + - type: recall_at_10 + value: 70.35900000000001 + - type: recall_at_100 + value: 91.642 + - type: recall_at_1000 + value: 97.977 + - type: recall_at_3 + value: 52.76 + - type: recall_at_5 + value: 61.148 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackEnglishRetrieval + config: default + split: test + revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 + metrics: + - type: map_at_1 + value: 35.884 + - type: map_at_10 + value: 48.14 + - type: map_at_100 + value: 49.5 + - type: map_at_1000 + value: 49.63 + - type: map_at_3 + value: 44.646 + - type: map_at_5 + value: 46.617999999999995 + - type: mrr_at_1 + value: 44.458999999999996 + - type: mrr_at_10 + value: 53.751000000000005 + - type: mrr_at_100 + value: 54.37800000000001 + - type: mrr_at_1000 + value: 54.415 + - type: mrr_at_3 + value: 51.815 + - type: mrr_at_5 + value: 52.882 + - type: ndcg_at_1 + value: 44.458999999999996 + - type: ndcg_at_10 + value: 54.157 + - type: ndcg_at_100 + value: 58.362 + - type: ndcg_at_1000 + value: 60.178 + - type: ndcg_at_3 + value: 49.661 + - type: ndcg_at_5 + value: 51.74999999999999 + - type: precision_at_1 + value: 44.458999999999996 + - type: precision_at_10 + value: 10.248 + - type: precision_at_100 + value: 1.5890000000000002 + - type: precision_at_1000 + value: 0.207 + - type: precision_at_3 + value: 23.928 + - type: precision_at_5 + value: 16.878999999999998 + - type: recall_at_1 + value: 35.884 + - type: recall_at_10 + value: 64.798 + - type: recall_at_100 + value: 82.345 + - type: recall_at_1000 + value: 93.267 + - type: recall_at_3 + value: 51.847 + - type: recall_at_5 + value: 57.601 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackGamingRetrieval + config: default + split: test + revision: 4885aa143210c98657558c04aaf3dc47cfb54340 + metrics: + - type: map_at_1 + value: 39.383 + - type: map_at_10 + value: 53.714 + - type: map_at_100 + value: 54.838 + - type: map_at_1000 + value: 54.87800000000001 + - type: map_at_3 + value: 50.114999999999995 + - type: map_at_5 + value: 52.153000000000006 + - type: mrr_at_1 + value: 45.016 + - type: mrr_at_10 + value: 56.732000000000006 + - type: mrr_at_100 + value: 57.411 + - type: mrr_at_1000 + value: 57.431 + - type: mrr_at_3 + value: 54.044000000000004 + - type: mrr_at_5 + value: 55.639 + - type: ndcg_at_1 + value: 45.016 + - type: ndcg_at_10 + value: 60.228 + - type: ndcg_at_100 + value: 64.277 + - type: ndcg_at_1000 + value: 65.07 + - type: ndcg_at_3 + value: 54.124 + - type: ndcg_at_5 + value: 57.147000000000006 + - type: precision_at_1 + value: 45.016 + - type: precision_at_10 + value: 9.937 + - type: precision_at_100 + value: 1.288 + - type: precision_at_1000 + value: 0.13899999999999998 + - type: precision_at_3 + value: 24.471999999999998 + - type: precision_at_5 + value: 16.991 + - type: recall_at_1 + value: 39.383 + - type: recall_at_10 + value: 76.175 + - type: recall_at_100 + value: 93.02 + - type: recall_at_1000 + value: 98.60900000000001 + - type: recall_at_3 + value: 60.265 + - type: recall_at_5 + value: 67.46600000000001 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackGisRetrieval + config: default + split: test + revision: 5003b3064772da1887988e05400cf3806fe491f2 + metrics: + - type: map_at_1 + value: 27.426000000000002 + - type: map_at_10 + value: 37.397000000000006 + - type: map_at_100 + value: 38.61 + - type: map_at_1000 + value: 38.678000000000004 + - type: map_at_3 + value: 34.150999999999996 + - type: map_at_5 + value: 36.137 + - type: mrr_at_1 + value: 29.944 + - type: mrr_at_10 + value: 39.654 + - type: mrr_at_100 + value: 40.638000000000005 + - type: mrr_at_1000 + value: 40.691 + - type: mrr_at_3 + value: 36.817 + - type: mrr_at_5 + value: 38.524 + - type: ndcg_at_1 + value: 29.944 + - type: ndcg_at_10 + value: 43.094 + - type: ndcg_at_100 + value: 48.789 + - type: ndcg_at_1000 + value: 50.339999999999996 + - type: ndcg_at_3 + value: 36.984 + - type: ndcg_at_5 + value: 40.248 + - type: precision_at_1 + value: 29.944 + - type: precision_at_10 + value: 6.78 + - type: precision_at_100 + value: 1.024 + - type: precision_at_1000 + value: 0.11800000000000001 + - type: precision_at_3 + value: 15.895000000000001 + - type: precision_at_5 + value: 11.39 + - type: recall_at_1 + value: 27.426000000000002 + - type: recall_at_10 + value: 58.464000000000006 + - type: recall_at_100 + value: 84.193 + - type: recall_at_1000 + value: 95.52000000000001 + - type: recall_at_3 + value: 42.172 + - type: recall_at_5 + value: 50.101 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackMathematicaRetrieval + config: default + split: test + revision: 90fceea13679c63fe563ded68f3b6f06e50061de + metrics: + - type: map_at_1 + value: 19.721 + - type: map_at_10 + value: 31.604 + - type: map_at_100 + value: 32.972 + - type: map_at_1000 + value: 33.077 + - type: map_at_3 + value: 27.218999999999998 + - type: map_at_5 + value: 29.53 + - type: mrr_at_1 + value: 25.0 + - type: mrr_at_10 + value: 35.843 + - type: mrr_at_100 + value: 36.785000000000004 + - type: mrr_at_1000 + value: 36.842000000000006 + - type: mrr_at_3 + value: 32.193 + - type: mrr_at_5 + value: 34.264 + - type: ndcg_at_1 + value: 25.0 + - type: ndcg_at_10 + value: 38.606 + - type: ndcg_at_100 + value: 44.272 + - type: ndcg_at_1000 + value: 46.527 + - type: ndcg_at_3 + value: 30.985000000000003 + - type: ndcg_at_5 + value: 34.43 + - type: precision_at_1 + value: 25.0 + - type: precision_at_10 + value: 7.811 + - type: precision_at_100 + value: 1.203 + - type: precision_at_1000 + value: 0.15 + - type: precision_at_3 + value: 15.423 + - type: precision_at_5 + value: 11.791 + - type: recall_at_1 + value: 19.721 + - type: recall_at_10 + value: 55.625 + - type: recall_at_100 + value: 79.34400000000001 + - type: recall_at_1000 + value: 95.208 + - type: recall_at_3 + value: 35.19 + - type: recall_at_5 + value: 43.626 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackPhysicsRetrieval + config: default + split: test + revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 + metrics: + - type: map_at_1 + value: 33.784 + - type: map_at_10 + value: 47.522 + - type: map_at_100 + value: 48.949999999999996 + - type: map_at_1000 + value: 49.038 + - type: map_at_3 + value: 43.284 + - type: map_at_5 + value: 45.629 + - type: mrr_at_1 + value: 41.482 + - type: mrr_at_10 + value: 52.830999999999996 + - type: mrr_at_100 + value: 53.559999999999995 + - type: mrr_at_1000 + value: 53.588 + - type: mrr_at_3 + value: 50.016000000000005 + - type: mrr_at_5 + value: 51.614000000000004 + - type: ndcg_at_1 + value: 41.482 + - type: ndcg_at_10 + value: 54.569 + - type: ndcg_at_100 + value: 59.675999999999995 + - type: ndcg_at_1000 + value: 60.989000000000004 + - type: ndcg_at_3 + value: 48.187000000000005 + - type: ndcg_at_5 + value: 51.183 + - type: precision_at_1 + value: 41.482 + - type: precision_at_10 + value: 10.221 + - type: precision_at_100 + value: 1.486 + - type: precision_at_1000 + value: 0.17500000000000002 + - type: precision_at_3 + value: 23.548 + - type: precision_at_5 + value: 16.805 + - type: recall_at_1 + value: 33.784 + - type: recall_at_10 + value: 69.798 + - type: recall_at_100 + value: 90.098 + - type: recall_at_1000 + value: 98.176 + - type: recall_at_3 + value: 52.127 + - type: recall_at_5 + value: 59.861 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackProgrammersRetrieval + config: default + split: test + revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 + metrics: + - type: map_at_1 + value: 28.038999999999998 + - type: map_at_10 + value: 41.904 + - type: map_at_100 + value: 43.36 + - type: map_at_1000 + value: 43.453 + - type: map_at_3 + value: 37.785999999999994 + - type: map_at_5 + value: 40.105000000000004 + - type: mrr_at_1 + value: 35.046 + - type: mrr_at_10 + value: 46.926 + - type: mrr_at_100 + value: 47.815000000000005 + - type: mrr_at_1000 + value: 47.849000000000004 + - type: mrr_at_3 + value: 44.273 + - type: mrr_at_5 + value: 45.774 + - type: ndcg_at_1 + value: 35.046 + - type: ndcg_at_10 + value: 48.937000000000005 + - type: ndcg_at_100 + value: 54.544000000000004 + - type: ndcg_at_1000 + value: 56.069 + - type: ndcg_at_3 + value: 42.858000000000004 + - type: ndcg_at_5 + value: 45.644 + - type: precision_at_1 + value: 35.046 + - type: precision_at_10 + value: 9.452 + - type: precision_at_100 + value: 1.429 + - type: precision_at_1000 + value: 0.173 + - type: precision_at_3 + value: 21.346999999999998 + - type: precision_at_5 + value: 15.342 + - type: recall_at_1 + value: 28.038999999999998 + - type: recall_at_10 + value: 64.59700000000001 + - type: recall_at_100 + value: 87.735 + - type: recall_at_1000 + value: 97.41300000000001 + - type: recall_at_3 + value: 47.368 + - type: recall_at_5 + value: 54.93900000000001 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackRetrieval + config: default + split: test + revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 + metrics: + - type: map_at_1 + value: 28.17291666666667 + - type: map_at_10 + value: 40.025749999999995 + - type: map_at_100 + value: 41.39208333333333 + - type: map_at_1000 + value: 41.499249999999996 + - type: map_at_3 + value: 36.347 + - type: map_at_5 + value: 38.41391666666667 + - type: mrr_at_1 + value: 33.65925 + - type: mrr_at_10 + value: 44.085499999999996 + - type: mrr_at_100 + value: 44.94116666666667 + - type: mrr_at_1000 + value: 44.9855 + - type: mrr_at_3 + value: 41.2815 + - type: mrr_at_5 + value: 42.91491666666666 + - type: ndcg_at_1 + value: 33.65925 + - type: ndcg_at_10 + value: 46.430833333333325 + - type: ndcg_at_100 + value: 51.761 + - type: ndcg_at_1000 + value: 53.50899999999999 + - type: ndcg_at_3 + value: 40.45133333333333 + - type: ndcg_at_5 + value: 43.31483333333334 + - type: precision_at_1 + value: 33.65925 + - type: precision_at_10 + value: 8.4995 + - type: precision_at_100 + value: 1.3210000000000004 + - type: precision_at_1000 + value: 0.16591666666666666 + - type: precision_at_3 + value: 19.165083333333335 + - type: precision_at_5 + value: 13.81816666666667 + - type: recall_at_1 + value: 28.17291666666667 + - type: recall_at_10 + value: 61.12624999999999 + - type: recall_at_100 + value: 83.97266666666667 + - type: recall_at_1000 + value: 95.66550000000001 + - type: recall_at_3 + value: 44.661249999999995 + - type: recall_at_5 + value: 51.983333333333334 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackStatsRetrieval + config: default + split: test + revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a + metrics: + - type: map_at_1 + value: 24.681 + - type: map_at_10 + value: 34.892 + - type: map_at_100 + value: 35.996 + - type: map_at_1000 + value: 36.083 + - type: map_at_3 + value: 31.491999999999997 + - type: map_at_5 + value: 33.632 + - type: mrr_at_1 + value: 28.528 + - type: mrr_at_10 + value: 37.694 + - type: mrr_at_100 + value: 38.613 + - type: mrr_at_1000 + value: 38.668 + - type: mrr_at_3 + value: 34.714 + - type: mrr_at_5 + value: 36.616 + - type: ndcg_at_1 + value: 28.528 + - type: ndcg_at_10 + value: 40.703 + - type: ndcg_at_100 + value: 45.993 + - type: ndcg_at_1000 + value: 47.847 + - type: ndcg_at_3 + value: 34.622 + - type: ndcg_at_5 + value: 38.035999999999994 + - type: precision_at_1 + value: 28.528 + - type: precision_at_10 + value: 6.902 + - type: precision_at_100 + value: 1.0370000000000001 + - type: precision_at_1000 + value: 0.126 + - type: precision_at_3 + value: 15.798000000000002 + - type: precision_at_5 + value: 11.655999999999999 + - type: recall_at_1 + value: 24.681 + - type: recall_at_10 + value: 55.81 + - type: recall_at_100 + value: 79.785 + - type: recall_at_1000 + value: 92.959 + - type: recall_at_3 + value: 39.074 + - type: recall_at_5 + value: 47.568 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackTexRetrieval + config: default + split: test + revision: 46989137a86843e03a6195de44b09deda022eec7 + metrics: + - type: map_at_1 + value: 18.627 + - type: map_at_10 + value: 27.872000000000003 + - type: map_at_100 + value: 29.237999999999996 + - type: map_at_1000 + value: 29.363 + - type: map_at_3 + value: 24.751 + - type: map_at_5 + value: 26.521 + - type: mrr_at_1 + value: 23.021 + - type: mrr_at_10 + value: 31.924000000000003 + - type: mrr_at_100 + value: 32.922000000000004 + - type: mrr_at_1000 + value: 32.988 + - type: mrr_at_3 + value: 29.192 + - type: mrr_at_5 + value: 30.798 + - type: ndcg_at_1 + value: 23.021 + - type: ndcg_at_10 + value: 33.535 + - type: ndcg_at_100 + value: 39.732 + - type: ndcg_at_1000 + value: 42.201 + - type: ndcg_at_3 + value: 28.153 + - type: ndcg_at_5 + value: 30.746000000000002 + - type: precision_at_1 + value: 23.021 + - type: precision_at_10 + value: 6.459 + - type: precision_at_100 + value: 1.1320000000000001 + - type: precision_at_1000 + value: 0.153 + - type: precision_at_3 + value: 13.719000000000001 + - type: precision_at_5 + value: 10.193000000000001 + - type: recall_at_1 + value: 18.627 + - type: recall_at_10 + value: 46.463 + - type: recall_at_100 + value: 74.226 + - type: recall_at_1000 + value: 91.28500000000001 + - type: recall_at_3 + value: 31.357000000000003 + - type: recall_at_5 + value: 38.067 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackUnixRetrieval + config: default + split: test + revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 + metrics: + - type: map_at_1 + value: 31.457 + - type: map_at_10 + value: 42.888 + - type: map_at_100 + value: 44.24 + - type: map_at_1000 + value: 44.327 + - type: map_at_3 + value: 39.588 + - type: map_at_5 + value: 41.423 + - type: mrr_at_1 + value: 37.126999999999995 + - type: mrr_at_10 + value: 47.083000000000006 + - type: mrr_at_100 + value: 47.997 + - type: mrr_at_1000 + value: 48.044 + - type: mrr_at_3 + value: 44.574000000000005 + - type: mrr_at_5 + value: 46.202 + - type: ndcg_at_1 + value: 37.126999999999995 + - type: ndcg_at_10 + value: 48.833 + - type: ndcg_at_100 + value: 54.327000000000005 + - type: ndcg_at_1000 + value: 56.011 + - type: ndcg_at_3 + value: 43.541999999999994 + - type: ndcg_at_5 + value: 46.127 + - type: precision_at_1 + value: 37.126999999999995 + - type: precision_at_10 + value: 8.376999999999999 + - type: precision_at_100 + value: 1.2309999999999999 + - type: precision_at_1000 + value: 0.146 + - type: precision_at_3 + value: 20.211000000000002 + - type: precision_at_5 + value: 14.16 + - type: recall_at_1 + value: 31.457 + - type: recall_at_10 + value: 62.369 + - type: recall_at_100 + value: 85.444 + - type: recall_at_1000 + value: 96.65599999999999 + - type: recall_at_3 + value: 47.961 + - type: recall_at_5 + value: 54.676 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackWebmastersRetrieval + config: default + split: test + revision: 160c094312a0e1facb97e55eeddb698c0abe3571 + metrics: + - type: map_at_1 + value: 27.139999999999997 + - type: map_at_10 + value: 38.801 + - type: map_at_100 + value: 40.549 + - type: map_at_1000 + value: 40.802 + - type: map_at_3 + value: 35.05 + - type: map_at_5 + value: 36.884 + - type: mrr_at_1 + value: 33.004 + - type: mrr_at_10 + value: 43.864 + - type: mrr_at_100 + value: 44.667 + - type: mrr_at_1000 + value: 44.717 + - type: mrr_at_3 + value: 40.777 + - type: mrr_at_5 + value: 42.319 + - type: ndcg_at_1 + value: 33.004 + - type: ndcg_at_10 + value: 46.022 + - type: ndcg_at_100 + value: 51.542 + - type: ndcg_at_1000 + value: 53.742000000000004 + - type: ndcg_at_3 + value: 39.795 + - type: ndcg_at_5 + value: 42.272 + - type: precision_at_1 + value: 33.004 + - type: precision_at_10 + value: 9.012 + - type: precision_at_100 + value: 1.7770000000000001 + - type: precision_at_1000 + value: 0.26 + - type: precision_at_3 + value: 19.038 + - type: precision_at_5 + value: 13.675999999999998 + - type: recall_at_1 + value: 27.139999999999997 + - type: recall_at_10 + value: 60.961 + - type: recall_at_100 + value: 84.451 + - type: recall_at_1000 + value: 98.113 + - type: recall_at_3 + value: 43.001 + - type: recall_at_5 + value: 49.896 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackWordpressRetrieval + config: default + split: test + revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 + metrics: + - type: map_at_1 + value: 17.936 + - type: map_at_10 + value: 27.399 + - type: map_at_100 + value: 28.632 + - type: map_at_1000 + value: 28.738000000000003 + - type: map_at_3 + value: 24.456 + - type: map_at_5 + value: 26.06 + - type: mrr_at_1 + value: 19.224 + - type: mrr_at_10 + value: 28.998 + - type: mrr_at_100 + value: 30.11 + - type: mrr_at_1000 + value: 30.177 + - type: mrr_at_3 + value: 26.247999999999998 + - type: mrr_at_5 + value: 27.708 + - type: ndcg_at_1 + value: 19.224 + - type: ndcg_at_10 + value: 32.911 + - type: ndcg_at_100 + value: 38.873999999999995 + - type: ndcg_at_1000 + value: 41.277 + - type: ndcg_at_3 + value: 27.142 + - type: ndcg_at_5 + value: 29.755 + - type: precision_at_1 + value: 19.224 + - type: precision_at_10 + value: 5.6930000000000005 + - type: precision_at_100 + value: 0.9259999999999999 + - type: precision_at_1000 + value: 0.126 + - type: precision_at_3 + value: 12.138 + - type: precision_at_5 + value: 8.909 + - type: recall_at_1 + value: 17.936 + - type: recall_at_10 + value: 48.096 + - type: recall_at_100 + value: 75.389 + - type: recall_at_1000 + value: 92.803 + - type: recall_at_3 + value: 32.812999999999995 + - type: recall_at_5 + value: 38.851 + - task: + type: Retrieval + dataset: + type: mteb/climate-fever + name: MTEB ClimateFEVER + config: default + split: test + revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 + metrics: + - type: map_at_1 + value: 22.076999999999998 + - type: map_at_10 + value: 35.44 + - type: map_at_100 + value: 37.651 + - type: map_at_1000 + value: 37.824999999999996 + - type: map_at_3 + value: 30.764999999999997 + - type: map_at_5 + value: 33.26 + - type: mrr_at_1 + value: 50.163000000000004 + - type: mrr_at_10 + value: 61.207 + - type: mrr_at_100 + value: 61.675000000000004 + - type: mrr_at_1000 + value: 61.692 + - type: mrr_at_3 + value: 58.60999999999999 + - type: mrr_at_5 + value: 60.307 + - type: ndcg_at_1 + value: 50.163000000000004 + - type: ndcg_at_10 + value: 45.882 + - type: ndcg_at_100 + value: 53.239999999999995 + - type: ndcg_at_1000 + value: 55.852000000000004 + - type: ndcg_at_3 + value: 40.514 + - type: ndcg_at_5 + value: 42.038 + - type: precision_at_1 + value: 50.163000000000004 + - type: precision_at_10 + value: 13.466000000000001 + - type: precision_at_100 + value: 2.164 + - type: precision_at_1000 + value: 0.266 + - type: precision_at_3 + value: 29.707 + - type: precision_at_5 + value: 21.694 + - type: recall_at_1 + value: 22.076999999999998 + - type: recall_at_10 + value: 50.193 + - type: recall_at_100 + value: 74.993 + - type: recall_at_1000 + value: 89.131 + - type: recall_at_3 + value: 35.472 + - type: recall_at_5 + value: 41.814 + - task: + type: Retrieval + dataset: + type: mteb/dbpedia + name: MTEB DBPedia + config: default + split: test + revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 + metrics: + - type: map_at_1 + value: 9.953 + - type: map_at_10 + value: 24.515 + - type: map_at_100 + value: 36.173 + - type: map_at_1000 + value: 38.351 + - type: map_at_3 + value: 16.592000000000002 + - type: map_at_5 + value: 20.036 + - type: mrr_at_1 + value: 74.25 + - type: mrr_at_10 + value: 81.813 + - type: mrr_at_100 + value: 82.006 + - type: mrr_at_1000 + value: 82.011 + - type: mrr_at_3 + value: 80.875 + - type: mrr_at_5 + value: 81.362 + - type: ndcg_at_1 + value: 62.5 + - type: ndcg_at_10 + value: 52.42 + - type: ndcg_at_100 + value: 56.808 + - type: ndcg_at_1000 + value: 63.532999999999994 + - type: ndcg_at_3 + value: 56.654 + - type: ndcg_at_5 + value: 54.18300000000001 + - type: precision_at_1 + value: 74.25 + - type: precision_at_10 + value: 42.699999999999996 + - type: precision_at_100 + value: 13.675 + - type: precision_at_1000 + value: 2.664 + - type: precision_at_3 + value: 60.5 + - type: precision_at_5 + value: 52.800000000000004 + - type: recall_at_1 + value: 9.953 + - type: recall_at_10 + value: 30.253999999999998 + - type: recall_at_100 + value: 62.516000000000005 + - type: recall_at_1000 + value: 84.163 + - type: recall_at_3 + value: 18.13 + - type: recall_at_5 + value: 22.771 + - task: + type: Classification + dataset: + type: mteb/emotion + name: MTEB EmotionClassification + config: default + split: test + revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 + metrics: + - type: accuracy + value: 79.455 + - type: f1 + value: 74.16798697647569 + - task: + type: Retrieval + dataset: + type: mteb/fever + name: MTEB FEVER + config: default + split: test + revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 + metrics: + - type: map_at_1 + value: 87.531 + - type: map_at_10 + value: 93.16799999999999 + - type: map_at_100 + value: 93.341 + - type: map_at_1000 + value: 93.349 + - type: map_at_3 + value: 92.444 + - type: map_at_5 + value: 92.865 + - type: mrr_at_1 + value: 94.014 + - type: mrr_at_10 + value: 96.761 + - type: mrr_at_100 + value: 96.762 + - type: mrr_at_1000 + value: 96.762 + - type: mrr_at_3 + value: 96.672 + - type: mrr_at_5 + value: 96.736 + - type: ndcg_at_1 + value: 94.014 + - type: ndcg_at_10 + value: 95.112 + - type: ndcg_at_100 + value: 95.578 + - type: ndcg_at_1000 + value: 95.68900000000001 + - type: ndcg_at_3 + value: 94.392 + - type: ndcg_at_5 + value: 94.72500000000001 + - type: precision_at_1 + value: 94.014 + - type: precision_at_10 + value: 11.065 + - type: precision_at_100 + value: 1.157 + - type: precision_at_1000 + value: 0.11800000000000001 + - type: precision_at_3 + value: 35.259 + - type: precision_at_5 + value: 21.599 + - type: recall_at_1 + value: 87.531 + - type: recall_at_10 + value: 97.356 + - type: recall_at_100 + value: 98.965 + - type: recall_at_1000 + value: 99.607 + - type: recall_at_3 + value: 95.312 + - type: recall_at_5 + value: 96.295 + - task: + type: Retrieval + dataset: + type: mteb/fiqa + name: MTEB FiQA2018 + config: default + split: test + revision: 27a168819829fe9bcd655c2df245fb19452e8e06 + metrics: + - type: map_at_1 + value: 32.055 + - type: map_at_10 + value: 53.114 + - type: map_at_100 + value: 55.235 + - type: map_at_1000 + value: 55.345 + - type: map_at_3 + value: 45.854 + - type: map_at_5 + value: 50.025 + - type: mrr_at_1 + value: 60.34 + - type: mrr_at_10 + value: 68.804 + - type: mrr_at_100 + value: 69.309 + - type: mrr_at_1000 + value: 69.32199999999999 + - type: mrr_at_3 + value: 66.40899999999999 + - type: mrr_at_5 + value: 67.976 + - type: ndcg_at_1 + value: 60.34 + - type: ndcg_at_10 + value: 62.031000000000006 + - type: ndcg_at_100 + value: 68.00500000000001 + - type: ndcg_at_1000 + value: 69.286 + - type: ndcg_at_3 + value: 56.355999999999995 + - type: ndcg_at_5 + value: 58.687 + - type: precision_at_1 + value: 60.34 + - type: precision_at_10 + value: 17.176 + - type: precision_at_100 + value: 2.36 + - type: precision_at_1000 + value: 0.259 + - type: precision_at_3 + value: 37.14 + - type: precision_at_5 + value: 27.809 + - type: recall_at_1 + value: 32.055 + - type: recall_at_10 + value: 70.91 + - type: recall_at_100 + value: 91.83 + - type: recall_at_1000 + value: 98.871 + - type: recall_at_3 + value: 51.202999999999996 + - type: recall_at_5 + value: 60.563 + - task: + type: Retrieval + dataset: + type: mteb/hotpotqa + name: MTEB HotpotQA + config: default + split: test + revision: ab518f4d6fcca38d87c25209f94beba119d02014 + metrics: + - type: map_at_1 + value: 43.68 + - type: map_at_10 + value: 64.389 + - type: map_at_100 + value: 65.24 + - type: map_at_1000 + value: 65.303 + - type: map_at_3 + value: 61.309000000000005 + - type: map_at_5 + value: 63.275999999999996 + - type: mrr_at_1 + value: 87.36 + - type: mrr_at_10 + value: 91.12 + - type: mrr_at_100 + value: 91.227 + - type: mrr_at_1000 + value: 91.229 + - type: mrr_at_3 + value: 90.57600000000001 + - type: mrr_at_5 + value: 90.912 + - type: ndcg_at_1 + value: 87.36 + - type: ndcg_at_10 + value: 73.076 + - type: ndcg_at_100 + value: 75.895 + - type: ndcg_at_1000 + value: 77.049 + - type: ndcg_at_3 + value: 68.929 + - type: ndcg_at_5 + value: 71.28 + - type: precision_at_1 + value: 87.36 + - type: precision_at_10 + value: 14.741000000000001 + - type: precision_at_100 + value: 1.694 + - type: precision_at_1000 + value: 0.185 + - type: precision_at_3 + value: 43.043 + - type: precision_at_5 + value: 27.681 + - type: recall_at_1 + value: 43.68 + - type: recall_at_10 + value: 73.707 + - type: recall_at_100 + value: 84.7 + - type: recall_at_1000 + value: 92.309 + - type: recall_at_3 + value: 64.564 + - type: recall_at_5 + value: 69.203 + - task: + type: Classification + dataset: + type: mteb/imdb + name: MTEB ImdbClassification + config: default + split: test + revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 + metrics: + - type: accuracy + value: 96.75399999999999 + - type: ap + value: 95.29389839242187 + - type: f1 + value: 96.75348377433475 + - task: + type: Retrieval + dataset: + type: mteb/msmarco + name: MTEB MSMARCO + config: default + split: dev + revision: c5a29a104738b98a9e76336939199e264163d4a0 + metrics: + - type: map_at_1 + value: 25.176 + - type: map_at_10 + value: 38.598 + - type: map_at_100 + value: 39.707 + - type: map_at_1000 + value: 39.744 + - type: map_at_3 + value: 34.566 + - type: map_at_5 + value: 36.863 + - type: mrr_at_1 + value: 25.874000000000002 + - type: mrr_at_10 + value: 39.214 + - type: mrr_at_100 + value: 40.251 + - type: mrr_at_1000 + value: 40.281 + - type: mrr_at_3 + value: 35.291 + - type: mrr_at_5 + value: 37.545 + - type: ndcg_at_1 + value: 25.874000000000002 + - type: ndcg_at_10 + value: 45.98 + - type: ndcg_at_100 + value: 51.197 + - type: ndcg_at_1000 + value: 52.073 + - type: ndcg_at_3 + value: 37.785999999999994 + - type: ndcg_at_5 + value: 41.870000000000005 + - type: precision_at_1 + value: 25.874000000000002 + - type: precision_at_10 + value: 7.181 + - type: precision_at_100 + value: 0.979 + - type: precision_at_1000 + value: 0.106 + - type: precision_at_3 + value: 16.051000000000002 + - type: precision_at_5 + value: 11.713 + - type: recall_at_1 + value: 25.176 + - type: recall_at_10 + value: 68.67699999999999 + - type: recall_at_100 + value: 92.55 + - type: recall_at_1000 + value: 99.164 + - type: recall_at_3 + value: 46.372 + - type: recall_at_5 + value: 56.16 + - task: + type: Classification + dataset: + type: mteb/mtop_domain + name: MTEB MTOPDomainClassification (en) + config: en + split: test + revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf + metrics: + - type: accuracy + value: 99.03784769721841 + - type: f1 + value: 98.97791641821495 + - task: + type: Classification + dataset: + type: mteb/mtop_intent + name: MTEB MTOPIntentClassification (en) + config: en + split: test + revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba + metrics: + - type: accuracy + value: 91.88326493388054 + - type: f1 + value: 73.74809928034335 + - task: + type: Classification + dataset: + type: mteb/amazon_massive_intent + name: MTEB MassiveIntentClassification (en) + config: en + split: test + revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 + metrics: + - type: accuracy + value: 85.41358439811701 + - type: f1 + value: 83.503679460639 + - task: + type: Classification + dataset: + type: mteb/amazon_massive_scenario + name: MTEB MassiveScenarioClassification (en) + config: en + split: test + revision: 7d571f92784cd94a019292a1f45445077d0ef634 + metrics: + - type: accuracy + value: 89.77135171486215 + - type: f1 + value: 88.89843747468366 + - task: + type: Clustering + dataset: + type: mteb/medrxiv-clustering-p2p + name: MTEB MedrxivClusteringP2P + config: default + split: test + revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 + metrics: + - type: v_measure + value: 46.22695362087359 + - task: + type: Clustering + dataset: + type: mteb/medrxiv-clustering-s2s + name: MTEB MedrxivClusteringS2S + config: default + split: test + revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 + metrics: + - type: v_measure + value: 44.132372165849425 + - task: + type: Reranking + dataset: + type: mteb/mind_small + name: MTEB MindSmallReranking + config: default + split: test + revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 + metrics: + - type: map + value: 33.35680810650402 + - type: mrr + value: 34.72625715637218 + - task: + type: Retrieval + dataset: + type: mteb/nfcorpus + name: MTEB NFCorpus + config: default + split: test + revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 + metrics: + - type: map_at_1 + value: 7.165000000000001 + - type: map_at_10 + value: 15.424 + - type: map_at_100 + value: 20.28 + - type: map_at_1000 + value: 22.065 + - type: map_at_3 + value: 11.236 + - type: map_at_5 + value: 13.025999999999998 + - type: mrr_at_1 + value: 51.702999999999996 + - type: mrr_at_10 + value: 59.965 + - type: mrr_at_100 + value: 60.667 + - type: mrr_at_1000 + value: 60.702999999999996 + - type: mrr_at_3 + value: 58.772000000000006 + - type: mrr_at_5 + value: 59.267 + - type: ndcg_at_1 + value: 49.536 + - type: ndcg_at_10 + value: 40.6 + - type: ndcg_at_100 + value: 37.848 + - type: ndcg_at_1000 + value: 46.657 + - type: ndcg_at_3 + value: 46.117999999999995 + - type: ndcg_at_5 + value: 43.619 + - type: precision_at_1 + value: 51.393 + - type: precision_at_10 + value: 30.31 + - type: precision_at_100 + value: 9.972 + - type: precision_at_1000 + value: 2.329 + - type: precision_at_3 + value: 43.137 + - type: precision_at_5 + value: 37.585 + - type: recall_at_1 + value: 7.165000000000001 + - type: recall_at_10 + value: 19.689999999999998 + - type: recall_at_100 + value: 39.237 + - type: recall_at_1000 + value: 71.417 + - type: recall_at_3 + value: 12.247 + - type: recall_at_5 + value: 14.902999999999999 + - task: + type: Retrieval + dataset: + type: mteb/nq + name: MTEB NQ + config: default + split: test + revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 + metrics: + - type: map_at_1 + value: 42.653999999999996 + - type: map_at_10 + value: 59.611999999999995 + - type: map_at_100 + value: 60.32300000000001 + - type: map_at_1000 + value: 60.336 + - type: map_at_3 + value: 55.584999999999994 + - type: map_at_5 + value: 58.19 + - type: mrr_at_1 + value: 47.683 + - type: mrr_at_10 + value: 62.06700000000001 + - type: mrr_at_100 + value: 62.537 + - type: mrr_at_1000 + value: 62.544999999999995 + - type: mrr_at_3 + value: 59.178 + - type: mrr_at_5 + value: 61.034 + - type: ndcg_at_1 + value: 47.654 + - type: ndcg_at_10 + value: 67.001 + - type: ndcg_at_100 + value: 69.73899999999999 + - type: ndcg_at_1000 + value: 69.986 + - type: ndcg_at_3 + value: 59.95700000000001 + - type: ndcg_at_5 + value: 64.025 + - type: precision_at_1 + value: 47.654 + - type: precision_at_10 + value: 10.367999999999999 + - type: precision_at_100 + value: 1.192 + - type: precision_at_1000 + value: 0.121 + - type: precision_at_3 + value: 26.651000000000003 + - type: precision_at_5 + value: 18.459 + - type: recall_at_1 + value: 42.653999999999996 + - type: recall_at_10 + value: 86.619 + - type: recall_at_100 + value: 98.04899999999999 + - type: recall_at_1000 + value: 99.812 + - type: recall_at_3 + value: 68.987 + - type: recall_at_5 + value: 78.158 + - task: + type: Retrieval + dataset: + type: mteb/quora + name: MTEB QuoraRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 72.538 + - type: map_at_10 + value: 86.702 + - type: map_at_100 + value: 87.31 + - type: map_at_1000 + value: 87.323 + - type: map_at_3 + value: 83.87 + - type: map_at_5 + value: 85.682 + - type: mrr_at_1 + value: 83.31 + - type: mrr_at_10 + value: 89.225 + - type: mrr_at_100 + value: 89.30399999999999 + - type: mrr_at_1000 + value: 89.30399999999999 + - type: mrr_at_3 + value: 88.44300000000001 + - type: mrr_at_5 + value: 89.005 + - type: ndcg_at_1 + value: 83.32000000000001 + - type: ndcg_at_10 + value: 90.095 + - type: ndcg_at_100 + value: 91.12 + - type: ndcg_at_1000 + value: 91.179 + - type: ndcg_at_3 + value: 87.606 + - type: ndcg_at_5 + value: 89.031 + - type: precision_at_1 + value: 83.32000000000001 + - type: precision_at_10 + value: 13.641 + - type: precision_at_100 + value: 1.541 + - type: precision_at_1000 + value: 0.157 + - type: precision_at_3 + value: 38.377 + - type: precision_at_5 + value: 25.162000000000003 + - type: recall_at_1 + value: 72.538 + - type: recall_at_10 + value: 96.47200000000001 + - type: recall_at_100 + value: 99.785 + - type: recall_at_1000 + value: 99.99900000000001 + - type: recall_at_3 + value: 89.278 + - type: recall_at_5 + value: 93.367 + - task: + type: Clustering + dataset: + type: mteb/reddit-clustering + name: MTEB RedditClustering + config: default + split: test + revision: 24640382cdbf8abc73003fb0fa6d111a705499eb + metrics: + - type: v_measure + value: 73.55219145406065 + - task: + type: Clustering + dataset: + type: mteb/reddit-clustering-p2p + name: MTEB RedditClusteringP2P + config: default + split: test + revision: 282350215ef01743dc01b456c7f5241fa8937f16 + metrics: + - type: v_measure + value: 74.13437105242755 + - task: + type: Retrieval + dataset: + type: mteb/scidocs + name: MTEB SCIDOCS + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 6.873 + - type: map_at_10 + value: 17.944 + - type: map_at_100 + value: 21.171 + - type: map_at_1000 + value: 21.528 + - type: map_at_3 + value: 12.415 + - type: map_at_5 + value: 15.187999999999999 + - type: mrr_at_1 + value: 33.800000000000004 + - type: mrr_at_10 + value: 46.455 + - type: mrr_at_100 + value: 47.378 + - type: mrr_at_1000 + value: 47.394999999999996 + - type: mrr_at_3 + value: 42.367 + - type: mrr_at_5 + value: 44.972 + - type: ndcg_at_1 + value: 33.800000000000004 + - type: ndcg_at_10 + value: 28.907 + - type: ndcg_at_100 + value: 39.695 + - type: ndcg_at_1000 + value: 44.582 + - type: ndcg_at_3 + value: 26.949 + - type: ndcg_at_5 + value: 23.988 + - type: precision_at_1 + value: 33.800000000000004 + - type: precision_at_10 + value: 15.079999999999998 + - type: precision_at_100 + value: 3.056 + - type: precision_at_1000 + value: 0.42100000000000004 + - type: precision_at_3 + value: 25.167 + - type: precision_at_5 + value: 21.26 + - type: recall_at_1 + value: 6.873 + - type: recall_at_10 + value: 30.568 + - type: recall_at_100 + value: 62.062 + - type: recall_at_1000 + value: 85.37700000000001 + - type: recall_at_3 + value: 15.312999999999999 + - type: recall_at_5 + value: 21.575 + - task: + type: STS + dataset: + type: mteb/sickr-sts + name: MTEB SICK-R + config: default + split: test + revision: a6ea5a8cab320b040a23452cc28066d9beae2cee + metrics: + - type: cos_sim_pearson + value: 82.37009118256057 + - type: cos_sim_spearman + value: 79.27986395671529 + - type: euclidean_pearson + value: 79.18037715442115 + - type: euclidean_spearman + value: 79.28004791561621 + - type: manhattan_pearson + value: 79.34062972800541 + - type: manhattan_spearman + value: 79.43106695543402 + - task: + type: STS + dataset: + type: mteb/sts12-sts + name: MTEB STS12 + config: default + split: test + revision: a0d554a64d88156834ff5ae9920b964011b16384 + metrics: + - type: cos_sim_pearson + value: 87.48474767383833 + - type: cos_sim_spearman + value: 79.54505388752513 + - type: euclidean_pearson + value: 83.43282704179565 + - type: euclidean_spearman + value: 79.54579919925405 + - type: manhattan_pearson + value: 83.77564492427952 + - type: manhattan_spearman + value: 79.84558396989286 + - task: + type: STS + dataset: + type: mteb/sts13-sts + name: MTEB STS13 + config: default + split: test + revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca + metrics: + - type: cos_sim_pearson + value: 88.803698035802 + - type: cos_sim_spearman + value: 88.83451367754881 + - type: euclidean_pearson + value: 88.28939285711628 + - type: euclidean_spearman + value: 88.83528996073112 + - type: manhattan_pearson + value: 88.28017412671795 + - type: manhattan_spearman + value: 88.9228828016344 + - task: + type: STS + dataset: + type: mteb/sts14-sts + name: MTEB STS14 + config: default + split: test + revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 + metrics: + - type: cos_sim_pearson + value: 85.27469288153428 + - type: cos_sim_spearman + value: 83.87477064876288 + - type: euclidean_pearson + value: 84.2601737035379 + - type: euclidean_spearman + value: 83.87431082479074 + - type: manhattan_pearson + value: 84.3621547772745 + - type: manhattan_spearman + value: 84.12094375000423 + - task: + type: STS + dataset: + type: mteb/sts15-sts + name: MTEB STS15 + config: default + split: test + revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 + metrics: + - type: cos_sim_pearson + value: 88.12749863201587 + - type: cos_sim_spearman + value: 88.54287568368565 + - type: euclidean_pearson + value: 87.90429700607999 + - type: euclidean_spearman + value: 88.5437689576261 + - type: manhattan_pearson + value: 88.19276653356833 + - type: manhattan_spearman + value: 88.99995393814679 + - task: + type: STS + dataset: + type: mteb/sts16-sts + name: MTEB STS16 + config: default + split: test + revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 + metrics: + - type: cos_sim_pearson + value: 85.68398747560902 + - type: cos_sim_spearman + value: 86.48815303460574 + - type: euclidean_pearson + value: 85.52356631237954 + - type: euclidean_spearman + value: 86.486391949551 + - type: manhattan_pearson + value: 85.67267981761788 + - type: manhattan_spearman + value: 86.7073696332485 + - task: + type: STS + dataset: + type: mteb/sts17-crosslingual-sts + name: MTEB STS17 (en-en) + config: en-en + split: test + revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d + metrics: + - type: cos_sim_pearson + value: 88.9057107443124 + - type: cos_sim_spearman + value: 88.7312168757697 + - type: euclidean_pearson + value: 88.72810439714794 + - type: euclidean_spearman + value: 88.71976185854771 + - type: manhattan_pearson + value: 88.50433745949111 + - type: manhattan_spearman + value: 88.51726175544195 + - task: + type: STS + dataset: + type: mteb/sts22-crosslingual-sts + name: MTEB STS22 (en) + config: en + split: test + revision: eea2b4fe26a775864c896887d910b76a8098ad3f + metrics: + - type: cos_sim_pearson + value: 67.59391795109886 + - type: cos_sim_spearman + value: 66.87613008631367 + - type: euclidean_pearson + value: 69.23198488262217 + - type: euclidean_spearman + value: 66.85427723013692 + - type: manhattan_pearson + value: 69.50730124841084 + - type: manhattan_spearman + value: 67.10404669820792 + - task: + type: STS + dataset: + type: mteb/stsbenchmark-sts + name: MTEB STSBenchmark + config: default + split: test + revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 + metrics: + - type: cos_sim_pearson + value: 87.0820605344619 + - type: cos_sim_spearman + value: 86.8518089863434 + - type: euclidean_pearson + value: 86.31087134689284 + - type: euclidean_spearman + value: 86.8518520517941 + - type: manhattan_pearson + value: 86.47203796160612 + - type: manhattan_spearman + value: 87.1080149734421 + - task: + type: Reranking + dataset: + type: mteb/scidocs-reranking + name: MTEB SciDocsRR + config: default + split: test + revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab + metrics: + - type: map + value: 89.09255369305481 + - type: mrr + value: 97.10323445617563 + - task: + type: Retrieval + dataset: + type: mteb/scifact + name: MTEB SciFact + config: default + split: test + revision: 0228b52cf27578f30900b9e5271d331663a030d7 + metrics: + - type: map_at_1 + value: 61.260999999999996 + - type: map_at_10 + value: 74.043 + - type: map_at_100 + value: 74.37700000000001 + - type: map_at_1000 + value: 74.384 + - type: map_at_3 + value: 71.222 + - type: map_at_5 + value: 72.875 + - type: mrr_at_1 + value: 64.333 + - type: mrr_at_10 + value: 74.984 + - type: mrr_at_100 + value: 75.247 + - type: mrr_at_1000 + value: 75.25500000000001 + - type: mrr_at_3 + value: 73.167 + - type: mrr_at_5 + value: 74.35000000000001 + - type: ndcg_at_1 + value: 64.333 + - type: ndcg_at_10 + value: 79.06 + - type: ndcg_at_100 + value: 80.416 + - type: ndcg_at_1000 + value: 80.55600000000001 + - type: ndcg_at_3 + value: 74.753 + - type: ndcg_at_5 + value: 76.97500000000001 + - type: precision_at_1 + value: 64.333 + - type: precision_at_10 + value: 10.567 + - type: precision_at_100 + value: 1.1199999999999999 + - type: precision_at_1000 + value: 0.11299999999999999 + - type: precision_at_3 + value: 29.889 + - type: precision_at_5 + value: 19.533 + - type: recall_at_1 + value: 61.260999999999996 + - type: recall_at_10 + value: 93.167 + - type: recall_at_100 + value: 99.0 + - type: recall_at_1000 + value: 100.0 + - type: recall_at_3 + value: 81.667 + - type: recall_at_5 + value: 87.394 + - task: + type: PairClassification + dataset: + type: mteb/sprintduplicatequestions-pairclassification + name: MTEB SprintDuplicateQuestions + config: default + split: test + revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 + metrics: + - type: cos_sim_accuracy + value: 99.71980198019801 + - type: cos_sim_ap + value: 92.81616007802704 + - type: cos_sim_f1 + value: 85.17548454688318 + - type: cos_sim_precision + value: 89.43894389438944 + - type: cos_sim_recall + value: 81.3 + - type: dot_accuracy + value: 99.71980198019801 + - type: dot_ap + value: 92.81398760591358 + - type: dot_f1 + value: 85.17548454688318 + - type: dot_precision + value: 89.43894389438944 + - type: dot_recall + value: 81.3 + - type: euclidean_accuracy + value: 99.71980198019801 + - type: euclidean_ap + value: 92.81560637245072 + - type: euclidean_f1 + value: 85.17548454688318 + - type: euclidean_precision + value: 89.43894389438944 + - type: euclidean_recall + value: 81.3 + - type: manhattan_accuracy + value: 99.73069306930694 + - type: manhattan_ap + value: 93.14005487480794 + - type: manhattan_f1 + value: 85.56263269639068 + - type: manhattan_precision + value: 91.17647058823529 + - type: manhattan_recall + value: 80.60000000000001 + - type: max_accuracy + value: 99.73069306930694 + - type: max_ap + value: 93.14005487480794 + - type: max_f1 + value: 85.56263269639068 + - task: + type: Clustering + dataset: + type: mteb/stackexchange-clustering + name: MTEB StackExchangeClustering + config: default + split: test + revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 + metrics: + - type: v_measure + value: 79.86443362395185 + - task: + type: Clustering + dataset: + type: mteb/stackexchange-clustering-p2p + name: MTEB StackExchangeClusteringP2P + config: default + split: test + revision: 815ca46b2622cec33ccafc3735d572c266efdb44 + metrics: + - type: v_measure + value: 49.40897096662564 + - task: + type: Reranking + dataset: + type: mteb/stackoverflowdupquestions-reranking + name: MTEB StackOverflowDupQuestions + config: default + split: test + revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 + metrics: + - type: map + value: 55.66040806627947 + - type: mrr + value: 56.58670475766064 + - task: + type: Summarization + dataset: + type: mteb/summeval + name: MTEB SummEval + config: default + split: test + revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c + metrics: + - type: cos_sim_pearson + value: 31.51015090598575 + - type: cos_sim_spearman + value: 31.35016454939226 + - type: dot_pearson + value: 31.5150068731 + - type: dot_spearman + value: 31.34790869023487 + - task: + type: Retrieval + dataset: + type: mteb/trec-covid + name: MTEB TRECCOVID + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 0.254 + - type: map_at_10 + value: 2.064 + - type: map_at_100 + value: 12.909 + - type: map_at_1000 + value: 31.761 + - type: map_at_3 + value: 0.738 + - type: map_at_5 + value: 1.155 + - type: mrr_at_1 + value: 96.0 + - type: mrr_at_10 + value: 98.0 + - type: mrr_at_100 + value: 98.0 + - type: mrr_at_1000 + value: 98.0 + - type: mrr_at_3 + value: 98.0 + - type: mrr_at_5 + value: 98.0 + - type: ndcg_at_1 + value: 93.0 + - type: ndcg_at_10 + value: 82.258 + - type: ndcg_at_100 + value: 64.34 + - type: ndcg_at_1000 + value: 57.912 + - type: ndcg_at_3 + value: 90.827 + - type: ndcg_at_5 + value: 86.79 + - type: precision_at_1 + value: 96.0 + - type: precision_at_10 + value: 84.8 + - type: precision_at_100 + value: 66.0 + - type: precision_at_1000 + value: 25.356 + - type: precision_at_3 + value: 94.667 + - type: precision_at_5 + value: 90.4 + - type: recall_at_1 + value: 0.254 + - type: recall_at_10 + value: 2.1950000000000003 + - type: recall_at_100 + value: 16.088 + - type: recall_at_1000 + value: 54.559000000000005 + - type: recall_at_3 + value: 0.75 + - type: recall_at_5 + value: 1.191 + - task: + type: Retrieval + dataset: + type: mteb/touche2020 + name: MTEB Touche2020 + config: default + split: test + revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f + metrics: + - type: map_at_1 + value: 2.976 + - type: map_at_10 + value: 11.389000000000001 + - type: map_at_100 + value: 18.429000000000002 + - type: map_at_1000 + value: 20.113 + - type: map_at_3 + value: 6.483 + - type: map_at_5 + value: 8.770999999999999 + - type: mrr_at_1 + value: 40.816 + - type: mrr_at_10 + value: 58.118 + - type: mrr_at_100 + value: 58.489999999999995 + - type: mrr_at_1000 + value: 58.489999999999995 + - type: mrr_at_3 + value: 53.061 + - type: mrr_at_5 + value: 57.041 + - type: ndcg_at_1 + value: 40.816 + - type: ndcg_at_10 + value: 30.567 + - type: ndcg_at_100 + value: 42.44 + - type: ndcg_at_1000 + value: 53.480000000000004 + - type: ndcg_at_3 + value: 36.016 + - type: ndcg_at_5 + value: 34.257 + - type: precision_at_1 + value: 42.857 + - type: precision_at_10 + value: 25.714 + - type: precision_at_100 + value: 8.429 + - type: precision_at_1000 + value: 1.5939999999999999 + - type: precision_at_3 + value: 36.735 + - type: precision_at_5 + value: 33.878 + - type: recall_at_1 + value: 2.976 + - type: recall_at_10 + value: 17.854999999999997 + - type: recall_at_100 + value: 51.833 + - type: recall_at_1000 + value: 86.223 + - type: recall_at_3 + value: 7.887 + - type: recall_at_5 + value: 12.026 + - task: + type: Classification + dataset: + type: mteb/toxic_conversations_50k + name: MTEB ToxicConversationsClassification + config: default + split: test + revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c + metrics: + - type: accuracy + value: 85.1174 + - type: ap + value: 30.169441069345748 + - type: f1 + value: 69.79254701873245 + - task: + type: Classification + dataset: + type: mteb/tweet_sentiment_extraction + name: MTEB TweetSentimentExtractionClassification + config: default + split: test + revision: d604517c81ca91fe16a244d1248fc021f9ecee7a + metrics: + - type: accuracy + value: 72.58347481607245 + - type: f1 + value: 72.74877295564937 + - task: + type: Clustering + dataset: + type: mteb/twentynewsgroups-clustering + name: MTEB TwentyNewsgroupsClustering + config: default + split: test + revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 + metrics: + - type: v_measure + value: 53.90586138221305 + - task: + type: PairClassification + dataset: + type: mteb/twittersemeval2015-pairclassification + name: MTEB TwitterSemEval2015 + config: default + split: test + revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 + metrics: + - type: cos_sim_accuracy + value: 87.35769207844072 + - type: cos_sim_ap + value: 77.9645072410354 + - type: cos_sim_f1 + value: 71.32352941176471 + - type: cos_sim_precision + value: 66.5903890160183 + - type: cos_sim_recall + value: 76.78100263852242 + - type: dot_accuracy + value: 87.37557370209214 + - type: dot_ap + value: 77.96250046429908 + - type: dot_f1 + value: 71.28932757557064 + - type: dot_precision + value: 66.95249130938586 + - type: dot_recall + value: 76.22691292875989 + - type: euclidean_accuracy + value: 87.35173153722357 + - type: euclidean_ap + value: 77.96520460741593 + - type: euclidean_f1 + value: 71.32470733210104 + - type: euclidean_precision + value: 66.91329479768785 + - type: euclidean_recall + value: 76.35883905013192 + - type: manhattan_accuracy + value: 87.25636287774931 + - type: manhattan_ap + value: 77.77752485611796 + - type: manhattan_f1 + value: 71.18148599269183 + - type: manhattan_precision + value: 66.10859728506787 + - type: manhattan_recall + value: 77.0976253298153 + - type: max_accuracy + value: 87.37557370209214 + - type: max_ap + value: 77.96520460741593 + - type: max_f1 + value: 71.32470733210104 + - task: + type: PairClassification + dataset: + type: mteb/twitterurlcorpus-pairclassification + name: MTEB TwitterURLCorpus + config: default + split: test + revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf + metrics: + - type: cos_sim_accuracy + value: 89.38176737687739 + - type: cos_sim_ap + value: 86.58811861657401 + - type: cos_sim_f1 + value: 79.09430644097604 + - type: cos_sim_precision + value: 75.45085977911366 + - type: cos_sim_recall + value: 83.10748383122882 + - type: dot_accuracy + value: 89.38370784336554 + - type: dot_ap + value: 86.58840606004333 + - type: dot_f1 + value: 79.10179860068133 + - type: dot_precision + value: 75.44546153308643 + - type: dot_recall + value: 83.13058207576223 + - type: euclidean_accuracy + value: 89.38564830985369 + - type: euclidean_ap + value: 86.58820721061164 + - type: euclidean_f1 + value: 79.09070942235888 + - type: euclidean_precision + value: 75.38729937194697 + - type: euclidean_recall + value: 83.17677856482906 + - type: manhattan_accuracy + value: 89.40699344122326 + - type: manhattan_ap + value: 86.60631843011362 + - type: manhattan_f1 + value: 79.14949970570925 + - type: manhattan_precision + value: 75.78191039729502 + - type: manhattan_recall + value: 82.83030489682784 + - type: max_accuracy + value: 89.40699344122326 + - type: max_ap + value: 86.60631843011362 + - type: max_f1 + value: 79.14949970570925 + - task: + type: STS + dataset: + type: C-MTEB/AFQMC + name: MTEB AFQMC + config: default + split: validation + revision: b44c3b011063adb25877c13823db83bb193913c4 + metrics: + - type: cos_sim_pearson + value: 65.58442135663871 + - type: cos_sim_spearman + value: 72.2538631361313 + - type: euclidean_pearson + value: 70.97255486607429 + - type: euclidean_spearman + value: 72.25374250228647 + - type: manhattan_pearson + value: 70.83250199989911 + - type: manhattan_spearman + value: 72.14819496536272 + - task: + type: STS + dataset: + type: C-MTEB/ATEC + name: MTEB ATEC + config: default + split: test + revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 + metrics: + - type: cos_sim_pearson + value: 59.99478404929932 + - type: cos_sim_spearman + value: 62.61836216999812 + - type: euclidean_pearson + value: 66.86429811933593 + - type: euclidean_spearman + value: 62.6183520374191 + - type: manhattan_pearson + value: 66.8063778911633 + - type: manhattan_spearman + value: 62.569607573241115 + - task: + type: Classification + dataset: + type: mteb/amazon_reviews_multi + name: MTEB AmazonReviewsClassification (zh) + config: zh + split: test + revision: 1399c76144fd37290681b995c656ef9b2e06e26d + metrics: + - type: accuracy + value: 53.98400000000001 + - type: f1 + value: 51.21447361350723 + - task: + type: STS + dataset: + type: C-MTEB/BQ + name: MTEB BQ + config: default + split: test + revision: e3dda5e115e487b39ec7e618c0c6a29137052a55 + metrics: + - type: cos_sim_pearson + value: 79.11941660686553 + - type: cos_sim_spearman + value: 81.25029594540435 + - type: euclidean_pearson + value: 82.06973504238826 + - type: euclidean_spearman + value: 81.2501989488524 + - type: manhattan_pearson + value: 82.10094630392753 + - type: manhattan_spearman + value: 81.27987244392389 + - task: + type: Clustering + dataset: + type: C-MTEB/CLSClusteringP2P + name: MTEB CLSClusteringP2P + config: default + split: test + revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 + metrics: + - type: v_measure + value: 47.07270168705156 + - task: + type: Clustering + dataset: + type: C-MTEB/CLSClusteringS2S + name: MTEB CLSClusteringS2S + config: default + split: test + revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f + metrics: + - type: v_measure + value: 45.98511703185043 + - task: + type: Reranking + dataset: + type: C-MTEB/CMedQAv1-reranking + name: MTEB CMedQAv1 + config: default + split: test + revision: 8d7f1e942507dac42dc58017c1a001c3717da7df + metrics: + - type: map + value: 88.19895157194931 + - type: mrr + value: 90.21424603174603 + - task: + type: Reranking + dataset: + type: C-MTEB/CMedQAv2-reranking + name: MTEB CMedQAv2 + config: default + split: test + revision: 23d186750531a14a0357ca22cd92d712fd512ea0 + metrics: + - type: map + value: 88.03317320980119 + - type: mrr + value: 89.9461507936508 + - task: + type: Retrieval + dataset: + type: C-MTEB/CmedqaRetrieval + name: MTEB CmedqaRetrieval + config: default + split: dev + revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 + metrics: + - type: map_at_1 + value: 29.037000000000003 + - type: map_at_10 + value: 42.001 + - type: map_at_100 + value: 43.773 + - type: map_at_1000 + value: 43.878 + - type: map_at_3 + value: 37.637 + - type: map_at_5 + value: 40.034 + - type: mrr_at_1 + value: 43.136 + - type: mrr_at_10 + value: 51.158 + - type: mrr_at_100 + value: 52.083 + - type: mrr_at_1000 + value: 52.12 + - type: mrr_at_3 + value: 48.733 + - type: mrr_at_5 + value: 50.025 + - type: ndcg_at_1 + value: 43.136 + - type: ndcg_at_10 + value: 48.685 + - type: ndcg_at_100 + value: 55.513 + - type: ndcg_at_1000 + value: 57.242000000000004 + - type: ndcg_at_3 + value: 43.329 + - type: ndcg_at_5 + value: 45.438 + - type: precision_at_1 + value: 43.136 + - type: precision_at_10 + value: 10.56 + - type: precision_at_100 + value: 1.6129999999999998 + - type: precision_at_1000 + value: 0.184 + - type: precision_at_3 + value: 24.064 + - type: precision_at_5 + value: 17.269000000000002 + - type: recall_at_1 + value: 29.037000000000003 + - type: recall_at_10 + value: 59.245000000000005 + - type: recall_at_100 + value: 87.355 + - type: recall_at_1000 + value: 98.74000000000001 + - type: recall_at_3 + value: 42.99 + - type: recall_at_5 + value: 49.681999999999995 + - task: + type: PairClassification + dataset: + type: C-MTEB/CMNLI + name: MTEB Cmnli + config: default + split: validation + revision: 41bc36f332156f7adc9e38f53777c959b2ae9766 + metrics: + - type: cos_sim_accuracy + value: 82.68190018039687 + - type: cos_sim_ap + value: 90.18017125327886 + - type: cos_sim_f1 + value: 83.64080906868193 + - type: cos_sim_precision + value: 79.7076890489303 + - type: cos_sim_recall + value: 87.98223053542202 + - type: dot_accuracy + value: 82.68190018039687 + - type: dot_ap + value: 90.18782350103646 + - type: dot_f1 + value: 83.64242087729039 + - type: dot_precision + value: 79.65313028764805 + - type: dot_recall + value: 88.05237315875614 + - type: euclidean_accuracy + value: 82.68190018039687 + - type: euclidean_ap + value: 90.1801957900632 + - type: euclidean_f1 + value: 83.63636363636364 + - type: euclidean_precision + value: 79.52772506852203 + - type: euclidean_recall + value: 88.19265840542437 + - type: manhattan_accuracy + value: 82.14070956103427 + - type: manhattan_ap + value: 89.96178420101427 + - type: manhattan_f1 + value: 83.21087838578791 + - type: manhattan_precision + value: 78.35605121850475 + - type: manhattan_recall + value: 88.70703764320785 + - type: max_accuracy + value: 82.68190018039687 + - type: max_ap + value: 90.18782350103646 + - type: max_f1 + value: 83.64242087729039 + - task: + type: Retrieval + dataset: + type: C-MTEB/CovidRetrieval + name: MTEB CovidRetrieval + config: default + split: dev + revision: 1271c7809071a13532e05f25fb53511ffce77117 + metrics: + - type: map_at_1 + value: 72.234 + - type: map_at_10 + value: 80.10000000000001 + - type: map_at_100 + value: 80.36 + - type: map_at_1000 + value: 80.363 + - type: map_at_3 + value: 78.315 + - type: map_at_5 + value: 79.607 + - type: mrr_at_1 + value: 72.392 + - type: mrr_at_10 + value: 80.117 + - type: mrr_at_100 + value: 80.36999999999999 + - type: mrr_at_1000 + value: 80.373 + - type: mrr_at_3 + value: 78.469 + - type: mrr_at_5 + value: 79.633 + - type: ndcg_at_1 + value: 72.392 + - type: ndcg_at_10 + value: 83.651 + - type: ndcg_at_100 + value: 84.749 + - type: ndcg_at_1000 + value: 84.83000000000001 + - type: ndcg_at_3 + value: 80.253 + - type: ndcg_at_5 + value: 82.485 + - type: precision_at_1 + value: 72.392 + - type: precision_at_10 + value: 9.557 + - type: precision_at_100 + value: 1.004 + - type: precision_at_1000 + value: 0.101 + - type: precision_at_3 + value: 28.732000000000003 + - type: precision_at_5 + value: 18.377 + - type: recall_at_1 + value: 72.234 + - type: recall_at_10 + value: 94.573 + - type: recall_at_100 + value: 99.368 + - type: recall_at_1000 + value: 100.0 + - type: recall_at_3 + value: 85.669 + - type: recall_at_5 + value: 91.01700000000001 + - task: + type: Retrieval + dataset: + type: C-MTEB/DuRetrieval + name: MTEB DuRetrieval + config: default + split: dev + revision: a1a333e290fe30b10f3f56498e3a0d911a693ced + metrics: + - type: map_at_1 + value: 26.173999999999996 + - type: map_at_10 + value: 80.04 + - type: map_at_100 + value: 82.94500000000001 + - type: map_at_1000 + value: 82.98100000000001 + - type: map_at_3 + value: 55.562999999999995 + - type: map_at_5 + value: 69.89800000000001 + - type: mrr_at_1 + value: 89.5 + - type: mrr_at_10 + value: 92.996 + - type: mrr_at_100 + value: 93.06400000000001 + - type: mrr_at_1000 + value: 93.065 + - type: mrr_at_3 + value: 92.658 + - type: mrr_at_5 + value: 92.84599999999999 + - type: ndcg_at_1 + value: 89.5 + - type: ndcg_at_10 + value: 87.443 + - type: ndcg_at_100 + value: 90.253 + - type: ndcg_at_1000 + value: 90.549 + - type: ndcg_at_3 + value: 85.874 + - type: ndcg_at_5 + value: 84.842 + - type: precision_at_1 + value: 89.5 + - type: precision_at_10 + value: 41.805 + - type: precision_at_100 + value: 4.827 + - type: precision_at_1000 + value: 0.49 + - type: precision_at_3 + value: 76.85 + - type: precision_at_5 + value: 64.8 + - type: recall_at_1 + value: 26.173999999999996 + - type: recall_at_10 + value: 89.101 + - type: recall_at_100 + value: 98.08099999999999 + - type: recall_at_1000 + value: 99.529 + - type: recall_at_3 + value: 57.902 + - type: recall_at_5 + value: 74.602 + - task: + type: Retrieval + dataset: + type: C-MTEB/EcomRetrieval + name: MTEB EcomRetrieval + config: default + split: dev + revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 + metrics: + - type: map_at_1 + value: 56.10000000000001 + - type: map_at_10 + value: 66.15299999999999 + - type: map_at_100 + value: 66.625 + - type: map_at_1000 + value: 66.636 + - type: map_at_3 + value: 63.632999999999996 + - type: map_at_5 + value: 65.293 + - type: mrr_at_1 + value: 56.10000000000001 + - type: mrr_at_10 + value: 66.15299999999999 + - type: mrr_at_100 + value: 66.625 + - type: mrr_at_1000 + value: 66.636 + - type: mrr_at_3 + value: 63.632999999999996 + - type: mrr_at_5 + value: 65.293 + - type: ndcg_at_1 + value: 56.10000000000001 + - type: ndcg_at_10 + value: 71.146 + - type: ndcg_at_100 + value: 73.27799999999999 + - type: ndcg_at_1000 + value: 73.529 + - type: ndcg_at_3 + value: 66.09 + - type: ndcg_at_5 + value: 69.08999999999999 + - type: precision_at_1 + value: 56.10000000000001 + - type: precision_at_10 + value: 8.68 + - type: precision_at_100 + value: 0.964 + - type: precision_at_1000 + value: 0.098 + - type: precision_at_3 + value: 24.4 + - type: precision_at_5 + value: 16.1 + - type: recall_at_1 + value: 56.10000000000001 + - type: recall_at_10 + value: 86.8 + - type: recall_at_100 + value: 96.39999999999999 + - type: recall_at_1000 + value: 98.3 + - type: recall_at_3 + value: 73.2 + - type: recall_at_5 + value: 80.5 + - task: + type: Classification + dataset: + type: C-MTEB/IFlyTek-classification + name: MTEB IFlyTek + config: default + split: validation + revision: 421605374b29664c5fc098418fe20ada9bd55f8a + metrics: + - type: accuracy + value: 54.52096960369373 + - type: f1 + value: 40.930845295808695 + - task: + type: Classification + dataset: + type: C-MTEB/JDReview-classification + name: MTEB JDReview + config: default + split: test + revision: b7c64bd89eb87f8ded463478346f76731f07bf8b + metrics: + - type: accuracy + value: 86.51031894934334 + - type: ap + value: 55.9516014323483 + - type: f1 + value: 81.54813679326381 + - task: + type: STS + dataset: + type: C-MTEB/LCQMC + name: MTEB LCQMC + config: default + split: test + revision: 17f9b096f80380fce5ed12a9be8be7784b337daf + metrics: + - type: cos_sim_pearson + value: 69.67437838574276 + - type: cos_sim_spearman + value: 73.81314174653045 + - type: euclidean_pearson + value: 72.63430276680275 + - type: euclidean_spearman + value: 73.81358736777001 + - type: manhattan_pearson + value: 72.58743833842829 + - type: manhattan_spearman + value: 73.7590419009179 + - task: + type: Reranking + dataset: + type: C-MTEB/Mmarco-reranking + name: MTEB MMarcoReranking + config: default + split: dev + revision: None + metrics: + - type: map + value: 31.648613483640254 + - type: mrr + value: 30.37420634920635 + - task: + type: Retrieval + dataset: + type: C-MTEB/MMarcoRetrieval + name: MTEB MMarcoRetrieval + config: default + split: dev + revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 + metrics: + - type: map_at_1 + value: 73.28099999999999 + - type: map_at_10 + value: 81.977 + - type: map_at_100 + value: 82.222 + - type: map_at_1000 + value: 82.22699999999999 + - type: map_at_3 + value: 80.441 + - type: map_at_5 + value: 81.46600000000001 + - type: mrr_at_1 + value: 75.673 + - type: mrr_at_10 + value: 82.41000000000001 + - type: mrr_at_100 + value: 82.616 + - type: mrr_at_1000 + value: 82.621 + - type: mrr_at_3 + value: 81.094 + - type: mrr_at_5 + value: 81.962 + - type: ndcg_at_1 + value: 75.673 + - type: ndcg_at_10 + value: 85.15599999999999 + - type: ndcg_at_100 + value: 86.151 + - type: ndcg_at_1000 + value: 86.26899999999999 + - type: ndcg_at_3 + value: 82.304 + - type: ndcg_at_5 + value: 84.009 + - type: precision_at_1 + value: 75.673 + - type: precision_at_10 + value: 10.042 + - type: precision_at_100 + value: 1.052 + - type: precision_at_1000 + value: 0.106 + - type: precision_at_3 + value: 30.673000000000002 + - type: precision_at_5 + value: 19.326999999999998 + - type: recall_at_1 + value: 73.28099999999999 + - type: recall_at_10 + value: 94.446 + - type: recall_at_100 + value: 98.737 + - type: recall_at_1000 + value: 99.649 + - type: recall_at_3 + value: 86.984 + - type: recall_at_5 + value: 91.024 + - task: + type: Classification + dataset: + type: mteb/amazon_massive_intent + name: MTEB MassiveIntentClassification (zh-CN) + config: zh-CN + split: test + revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 + metrics: + - type: accuracy + value: 81.08607935440484 + - type: f1 + value: 78.24879986066307 + - task: + type: Classification + dataset: + type: mteb/amazon_massive_scenario + name: MTEB MassiveScenarioClassification (zh-CN) + config: zh-CN + split: test + revision: 7d571f92784cd94a019292a1f45445077d0ef634 + metrics: + - type: accuracy + value: 86.05917955615332 + - type: f1 + value: 85.05279279434997 + - task: + type: Retrieval + dataset: + type: C-MTEB/MedicalRetrieval + name: MTEB MedicalRetrieval + config: default + split: dev + revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 + metrics: + - type: map_at_1 + value: 56.2 + - type: map_at_10 + value: 62.57899999999999 + - type: map_at_100 + value: 63.154999999999994 + - type: map_at_1000 + value: 63.193 + - type: map_at_3 + value: 61.217 + - type: map_at_5 + value: 62.012 + - type: mrr_at_1 + value: 56.3 + - type: mrr_at_10 + value: 62.629000000000005 + - type: mrr_at_100 + value: 63.205999999999996 + - type: mrr_at_1000 + value: 63.244 + - type: mrr_at_3 + value: 61.267 + - type: mrr_at_5 + value: 62.062 + - type: ndcg_at_1 + value: 56.2 + - type: ndcg_at_10 + value: 65.592 + - type: ndcg_at_100 + value: 68.657 + - type: ndcg_at_1000 + value: 69.671 + - type: ndcg_at_3 + value: 62.808 + - type: ndcg_at_5 + value: 64.24499999999999 + - type: precision_at_1 + value: 56.2 + - type: precision_at_10 + value: 7.5 + - type: precision_at_100 + value: 0.899 + - type: precision_at_1000 + value: 0.098 + - type: precision_at_3 + value: 22.467000000000002 + - type: precision_at_5 + value: 14.180000000000001 + - type: recall_at_1 + value: 56.2 + - type: recall_at_10 + value: 75.0 + - type: recall_at_100 + value: 89.9 + - type: recall_at_1000 + value: 97.89999999999999 + - type: recall_at_3 + value: 67.4 + - type: recall_at_5 + value: 70.89999999999999 + - task: + type: Classification + dataset: + type: C-MTEB/MultilingualSentiment-classification + name: MTEB MultilingualSentiment + config: default + split: validation + revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a + metrics: + - type: accuracy + value: 76.87666666666667 + - type: f1 + value: 76.7317686219665 + - task: + type: PairClassification + dataset: + type: C-MTEB/OCNLI + name: MTEB Ocnli + config: default + split: validation + revision: 66e76a618a34d6d565d5538088562851e6daa7ec + metrics: + - type: cos_sim_accuracy + value: 79.64266377910124 + - type: cos_sim_ap + value: 84.78274442344829 + - type: cos_sim_f1 + value: 81.16947472745292 + - type: cos_sim_precision + value: 76.47058823529412 + - type: cos_sim_recall + value: 86.48363252375924 + - type: dot_accuracy + value: 79.64266377910124 + - type: dot_ap + value: 84.7851404063692 + - type: dot_f1 + value: 81.16947472745292 + - type: dot_precision + value: 76.47058823529412 + - type: dot_recall + value: 86.48363252375924 + - type: euclidean_accuracy + value: 79.64266377910124 + - type: euclidean_ap + value: 84.78068373762378 + - type: euclidean_f1 + value: 81.14794656110837 + - type: euclidean_precision + value: 76.35009310986965 + - type: euclidean_recall + value: 86.58922914466737 + - type: manhattan_accuracy + value: 79.48023822414727 + - type: manhattan_ap + value: 84.72928897427576 + - type: manhattan_f1 + value: 81.32084770823064 + - type: manhattan_precision + value: 76.24768946395564 + - type: manhattan_recall + value: 87.11721224920802 + - type: max_accuracy + value: 79.64266377910124 + - type: max_ap + value: 84.7851404063692 + - type: max_f1 + value: 81.32084770823064 + - task: + type: Classification + dataset: + type: C-MTEB/OnlineShopping-classification + name: MTEB OnlineShopping + config: default + split: test + revision: e610f2ebd179a8fda30ae534c3878750a96db120 + metrics: + - type: accuracy + value: 94.3 + - type: ap + value: 92.8664032274438 + - type: f1 + value: 94.29311102997727 + - task: + type: STS + dataset: + type: C-MTEB/PAWSX + name: MTEB PAWSX + config: default + split: test + revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1 + metrics: + - type: cos_sim_pearson + value: 48.51392279882909 + - type: cos_sim_spearman + value: 54.06338895994974 + - type: euclidean_pearson + value: 52.58480559573412 + - type: euclidean_spearman + value: 54.06417276612201 + - type: manhattan_pearson + value: 52.69525121721343 + - type: manhattan_spearman + value: 54.048147455389675 + - task: + type: STS + dataset: + type: C-MTEB/QBQTC + name: MTEB QBQTC + config: default + split: test + revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7 + metrics: + - type: cos_sim_pearson + value: 29.728387290757325 + - type: cos_sim_spearman + value: 31.366121633635284 + - type: euclidean_pearson + value: 29.14588368552961 + - type: euclidean_spearman + value: 31.36764411112844 + - type: manhattan_pearson + value: 29.63517350523121 + - type: manhattan_spearman + value: 31.94157020583762 + - task: + type: STS + dataset: + type: mteb/sts22-crosslingual-sts + name: MTEB STS22 (zh) + config: zh + split: test + revision: eea2b4fe26a775864c896887d910b76a8098ad3f + metrics: + - type: cos_sim_pearson + value: 63.64868296271406 + - type: cos_sim_spearman + value: 66.12800618164744 + - type: euclidean_pearson + value: 63.21405767340238 + - type: euclidean_spearman + value: 66.12786567790748 + - type: manhattan_pearson + value: 64.04300276525848 + - type: manhattan_spearman + value: 66.5066857145652 + - task: + type: STS + dataset: + type: C-MTEB/STSB + name: MTEB STSB + config: default + split: test + revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0 + metrics: + - type: cos_sim_pearson + value: 81.2302623912794 + - type: cos_sim_spearman + value: 81.16833673266562 + - type: euclidean_pearson + value: 79.47647843876024 + - type: euclidean_spearman + value: 81.16944349524972 + - type: manhattan_pearson + value: 79.84947238492208 + - type: manhattan_spearman + value: 81.64626599410026 + - task: + type: Reranking + dataset: + type: C-MTEB/T2Reranking + name: MTEB T2Reranking + config: default + split: dev + revision: 76631901a18387f85eaa53e5450019b87ad58ef9 + metrics: + - type: map + value: 67.80129586475687 + - type: mrr + value: 77.77402311635554 + - task: + type: Retrieval + dataset: + type: C-MTEB/T2Retrieval + name: MTEB T2Retrieval + config: default + split: dev + revision: 8731a845f1bf500a4f111cf1070785c793d10e64 + metrics: + - type: map_at_1 + value: 28.666999999999998 + - type: map_at_10 + value: 81.063 + - type: map_at_100 + value: 84.504 + - type: map_at_1000 + value: 84.552 + - type: map_at_3 + value: 56.897 + - type: map_at_5 + value: 70.073 + - type: mrr_at_1 + value: 92.087 + - type: mrr_at_10 + value: 94.132 + - type: mrr_at_100 + value: 94.19800000000001 + - type: mrr_at_1000 + value: 94.19999999999999 + - type: mrr_at_3 + value: 93.78999999999999 + - type: mrr_at_5 + value: 94.002 + - type: ndcg_at_1 + value: 92.087 + - type: ndcg_at_10 + value: 87.734 + - type: ndcg_at_100 + value: 90.736 + - type: ndcg_at_1000 + value: 91.184 + - type: ndcg_at_3 + value: 88.78 + - type: ndcg_at_5 + value: 87.676 + - type: precision_at_1 + value: 92.087 + - type: precision_at_10 + value: 43.46 + - type: precision_at_100 + value: 5.07 + - type: precision_at_1000 + value: 0.518 + - type: precision_at_3 + value: 77.49000000000001 + - type: precision_at_5 + value: 65.194 + - type: recall_at_1 + value: 28.666999999999998 + - type: recall_at_10 + value: 86.632 + - type: recall_at_100 + value: 96.646 + - type: recall_at_1000 + value: 98.917 + - type: recall_at_3 + value: 58.333999999999996 + - type: recall_at_5 + value: 72.974 + - task: + type: Classification + dataset: + type: C-MTEB/TNews-classification + name: MTEB TNews + config: default + split: validation + revision: 317f262bf1e6126357bbe89e875451e4b0938fe4 + metrics: + - type: accuracy + value: 52.971999999999994 + - type: f1 + value: 50.2898280984929 + - task: + type: Clustering + dataset: + type: C-MTEB/ThuNewsClusteringP2P + name: MTEB ThuNewsClusteringP2P + config: default + split: test + revision: 5798586b105c0434e4f0fe5e767abe619442cf93 + metrics: + - type: v_measure + value: 86.0797948663824 + - task: + type: Clustering + dataset: + type: C-MTEB/ThuNewsClusteringS2S + name: MTEB ThuNewsClusteringS2S + config: default + split: test + revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d + metrics: + - type: v_measure + value: 85.10759092255017 + - task: + type: Retrieval + dataset: + type: C-MTEB/VideoRetrieval + name: MTEB VideoRetrieval + config: default + split: dev + revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 + metrics: + - type: map_at_1 + value: 65.60000000000001 + - type: map_at_10 + value: 74.773 + - type: map_at_100 + value: 75.128 + - type: map_at_1000 + value: 75.136 + - type: map_at_3 + value: 73.05 + - type: map_at_5 + value: 74.13499999999999 + - type: mrr_at_1 + value: 65.60000000000001 + - type: mrr_at_10 + value: 74.773 + - type: mrr_at_100 + value: 75.128 + - type: mrr_at_1000 + value: 75.136 + - type: mrr_at_3 + value: 73.05 + - type: mrr_at_5 + value: 74.13499999999999 + - type: ndcg_at_1 + value: 65.60000000000001 + - type: ndcg_at_10 + value: 78.84299999999999 + - type: ndcg_at_100 + value: 80.40899999999999 + - type: ndcg_at_1000 + value: 80.57 + - type: ndcg_at_3 + value: 75.40599999999999 + - type: ndcg_at_5 + value: 77.351 + - type: precision_at_1 + value: 65.60000000000001 + - type: precision_at_10 + value: 9.139999999999999 + - type: precision_at_100 + value: 0.984 + - type: precision_at_1000 + value: 0.1 + - type: precision_at_3 + value: 27.400000000000002 + - type: precision_at_5 + value: 17.380000000000003 + - type: recall_at_1 + value: 65.60000000000001 + - type: recall_at_10 + value: 91.4 + - type: recall_at_100 + value: 98.4 + - type: recall_at_1000 + value: 99.6 + - type: recall_at_3 + value: 82.19999999999999 + - type: recall_at_5 + value: 86.9 + - task: + type: Classification + dataset: + type: C-MTEB/waimai-classification + name: MTEB Waimai + config: default + split: test + revision: 339287def212450dcaa9df8c22bf93e9980c7023 + metrics: + - type: accuracy + value: 89.47 + - type: ap + value: 75.59561751845389 + - type: f1 + value: 87.95207751382563 +--- + +## gte-Qwen2-7B-instruct + +**gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard) (as of June 16, 2024). + +Recently, the [**Qwen team**](https://huggingface.co/Qwen) released the Qwen2 series models, and we have trained the **gte-Qwen2-7B-instruct** model based on the [Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) LLM model. Compared to the [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) model, the **gte-Qwen2-7B-instruct** model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models. + +The model incorporates several key advancements: + +- Integration of bidirectional attention mechanisms, enriching its contextual understanding. +- Instruction tuning, applied solely on the query side for streamlined efficiency +- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks. + + +## Model Information +- Model Size: 7B +- Embedding Dimension: 3584 +- Max Input Tokens: 32k + +## Requirements +``` +transformers>=4.39.2 +flash_attn>=2.5.6 +``` +## Usage + +### Sentence Transformers + +```python +from sentence_transformers import SentenceTransformer + +model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True) +# In case you want to reduce the maximum length: +model.max_seq_length = 8192 + +queries = [ + "how much protein should a female eat", + "summit define", +] +documents = [ + "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.", + "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.", +] + +query_embeddings = model.encode(queries, prompt_name="query") +document_embeddings = model.encode(documents) + +scores = (query_embeddings @ document_embeddings.T) * 100 +print(scores.tolist()) +``` + +Observe the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice. + +### Transformers + +```python +import torch +import torch.nn.functional as F + +from torch import Tensor +from transformers import AutoTokenizer, AutoModel + + +def last_token_pool(last_hidden_states: Tensor, + attention_mask: Tensor) -> Tensor: + left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) + if left_padding: + return last_hidden_states[:, -1] + else: + sequence_lengths = attention_mask.sum(dim=1) - 1 + batch_size = last_hidden_states.shape[0] + return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] + + +def get_detailed_instruct(task_description: str, query: str) -> str: + return f'Instruct: {task_description}\nQuery: {query}' + + +# Each query must come with a one-sentence instruction that describes the task +task = 'Given a web search query, retrieve relevant passages that answer the query' +queries = [ + get_detailed_instruct(task, 'how much protein should a female eat'), + get_detailed_instruct(task, 'summit define') +] +# No need to add instruction for retrieval documents +documents = [ + "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.", + "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." +] +input_texts = queries + documents + +tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True) +model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True) + +max_length = 8192 + +# Tokenize the input texts +batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt') +outputs = model(**batch_dict) +embeddings = last_token_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()) +``` + +## Evaluation + +### MTEB & C-MTEB + +You can use the [scripts/eval_mteb.py](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of **gte-Qwen2-7B-instruct** on MTEB(English)/C-MTEB(Chinese): + +| Model Name | MTEB(56) | C-MTEB(35) | +|:----:|:---------:|:----------:| +| [bge-base-en-1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 64.23 | - | +| [bge-large-en-1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 63.55 | - | +| [gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 65.39 | - | +| [gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 64.11 | - | +| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 64.68 | - | +| [acge_text_embedding](https://huggingface.co/aspire/acge_text_embedding) | - | 69.07 | +| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) | - | 68.55 | +| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | - | 66.72 | +| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 59.45 | 56.21 | +| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 61.50 | 58.81 | +| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 66.63 | 60.81 | +| [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | 67.34 | 69.52 | +| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 69.32 | - | +| [**gte-Qwen2-7B-instruct**](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | **70.24** | **72.05** | + +### GTE Models + +The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture). + +| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) | +|:-------------------------------------------------------------------------------------:|:--------:|:-----: |:---------:|:-------------------------------:| +| [GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 1.25GB | +| [GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.41GB | +| [GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.12GB | +| [GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 1.25GB | +| [GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB | +| [GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB | +| [GTE-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 8192 | 1024 | 1.74GB | +| [GTE-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 8192 | 768 | 0.51GB | +| [GTE-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct) | Multilingual | 32000 | 4096 | 26.45GB | +| [GTE-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) | Multilingual | 32000 | 3584 | 26.45GB | + +## Citation + +If you find our paper or models helpful, please consider cite: + +``` +@article{li2023towards, + title={Towards general text embeddings with multi-stage contrastive learning}, + author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, + journal={arXiv preprint arXiv:2308.03281}, + year={2023} +} +``` +