richarderkhov/gritlm_-_gritlm-7b-gguf overview

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  "metadata": {},
  "card_data": {
    "frontmatter": {},
    "hero_image_url": "",
    "summary": "> GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks. | Model | Description | |-------|-------------| | GritLM 7B | Mistral 7B finetuned using GRIT | | GritLM 8x7B | Mixtral 8x7B finetuned using GRIT | # Use The model usage is documented here. # Citation ``bibtex @misc{muennighoff2024generative, title={Generative Representational Instruction Tuning}, author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela}, year={2024}, eprint={2402.09906}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "Quantization made by Richard Erkhov.\n\n[Github](https://github.com/RichardErkhov)\n\n[Discord](https://discord.gg/pvy7H8DZMG)\n\n[Request more models](https://github.com/RichardErkhov/quant_request)\n\n\nGritLM-7B - GGUF\n- Model creator: https://huggingface.co/GritLM/\n- Original model: https://huggingface.co/GritLM/GritLM-7B/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [GritLM-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q2_K.gguf) | Q2_K | 2.53GB |\n| [GritLM-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.IQ3_XS.gguf) | IQ3_XS | 2.81GB |\n| [GritLM-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.IQ3_S.gguf) | IQ3_S | 2.96GB |\n| [GritLM-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB |\n| [GritLM-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.IQ3_M.gguf) | IQ3_M | 3.06GB |\n| [GritLM-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q3_K.gguf) | Q3_K | 3.28GB |\n| [GritLM-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q3_K_M.gguf) | Q3_K_M | 3.28GB |\n| [GritLM-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q3_K_L.gguf) | Q3_K_L | 3.56GB |\n| [GritLM-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.IQ4_XS.gguf) | IQ4_XS | 3.67GB |\n| [GritLM-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q4_0.gguf) | Q4_0 | 3.83GB |\n| [GritLM-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.IQ4_NL.gguf) | IQ4_NL | 3.87GB |\n| [GritLM-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB |\n| [GritLM-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q4_K.gguf) | Q4_K | 4.07GB |\n| [GritLM-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q4_K_M.gguf) | Q4_K_M | 4.07GB |\n| [GritLM-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q4_1.gguf) | Q4_1 | 4.24GB |\n| [GritLM-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q5_0.gguf) | Q5_0 | 4.65GB |\n| [GritLM-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB |\n| [GritLM-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q5_K.gguf) | Q5_K | 4.78GB |\n| [GritLM-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q5_K_M.gguf) | Q5_K_M | 4.78GB |\n| [GritLM-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q5_1.gguf) | Q5_1 | 5.07GB |\n| [GritLM-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/GritLM_-_GritLM-7B-gguf/blob/main/GritLM-7B.Q6_K.gguf) | Q6_K | 5.53GB |\n\n\n\n\nOriginal model description:\n---\npipeline_tag: text-generation\ninference: true\nlicense: apache-2.0\ndatasets:\n- GritLM/tulu2\ntags:\n- mteb\nmodel-index:\n- name: GritLM-7B\n  results:\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/amazon_counterfactual\n      name: MTEB AmazonCounterfactualClassification (en)\n      config: en\n      split: test\n      revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n    metrics:\n    - type: accuracy\n      value: 81.17910447761194\n    - type: ap\n      value: 46.26260671758199\n    - type: f1\n      value: 75.44565719934167\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/amazon_polarity\n      name: MTEB AmazonPolarityClassification\n      config: default\n      split: test\n      revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n    metrics:\n    - type: accuracy\n      value: 96.5161\n    - type: ap\n      value: 94.79131981460425\n    - type: f1\n      value: 96.51506148413065\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/amazon_reviews_multi\n      name: MTEB AmazonReviewsClassification (en)\n      config: en\n      split: test\n      revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n    metrics:\n    - type: accuracy\n      value: 57.806000000000004\n    - type: f1\n      value: 56.78350156257903\n  - task:\n      type: Retrieval\n    dataset:\n      type: arguana\n      name: MTEB ArguAna\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 38.478\n    - type: map_at_10\n      value: 54.955\n    - type: map_at_100\n      value: 54.955\n    - type: map_at_1000\n      value: 54.955\n    - type: map_at_3\n      value: 50.888999999999996\n    - type: map_at_5\n      value: 53.349999999999994\n    - type: mrr_at_1\n      value: 39.757999999999996\n    - type: mrr_at_10\n      value: 55.449000000000005\n    - type: mrr_at_100\n      value: 55.449000000000005\n    - type: mrr_at_1000\n      value: 55.449000000000005\n    - type: mrr_at_3\n      value: 51.37500000000001\n    - type: mrr_at_5\n      value: 53.822\n    - type: ndcg_at_1\n      value: 38.478\n    - type: ndcg_at_10\n      value: 63.239999999999995\n    - type: ndcg_at_100\n      value: 63.239999999999995\n    - type: ndcg_at_1000\n      value: 63.239999999999995\n    - type: ndcg_at_3\n      value: 54.935\n    - type: ndcg_at_5\n      value: 59.379000000000005\n    - type: precision_at_1\n      value: 38.478\n    - type: precision_at_10\n      value: 8.933\n    - type: precision_at_100\n      value: 0.893\n    - type: precision_at_1000\n      value: 0.089\n    - type: precision_at_3\n      value: 22.214\n    - type: precision_at_5\n      value: 15.491\n    - type: recall_at_1\n      value: 38.478\n    - type: recall_at_10\n      value: 89.331\n    - type: recall_at_100\n      value: 89.331\n    - type: recall_at_1000\n      value: 89.331\n    - type: recall_at_3\n      value: 66.643\n    - type: recall_at_5\n      value: 77.45400000000001\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/arxiv-clustering-p2p\n      name: MTEB ArxivClusteringP2P\n      config: default\n      split: test\n      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n    metrics:\n    - type: v_measure\n      value: 51.67144081472449\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/arxiv-clustering-s2s\n      name: MTEB ArxivClusteringS2S\n      config: default\n      split: test\n      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n    metrics:\n    - type: v_measure\n      value: 48.11256154264126\n  - task:\n      type: Reranking\n    dataset:\n      type: mteb/askubuntudupquestions-reranking\n      name: MTEB AskUbuntuDupQuestions\n      config: default\n      split: test\n      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n    metrics:\n    - type: map\n      value: 67.33801955487878\n    - type: mrr\n      value: 80.71549487754474\n  - task:\n      type: STS\n    dataset:\n      type: mteb/biosses-sts\n      name: MTEB BIOSSES\n      config: default\n      split: test\n      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n    metrics:\n    - type: cos_sim_pearson\n      value: 88.1935203751726\n    - type: cos_sim_spearman\n      value: 86.35497970498659\n    - type: euclidean_pearson\n      value: 85.46910708503744\n    - type: euclidean_spearman\n      value: 85.13928935405485\n    - type: manhattan_pearson\n      value: 85.68373836333303\n    - type: manhattan_spearman\n      value: 85.40013867117746\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/banking77\n      name: MTEB Banking77Classification\n      config: default\n      split: test\n      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n    metrics:\n    - type: accuracy\n      value: 88.46753246753248\n    - type: f1\n      value: 88.43006344981134\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/biorxiv-clustering-p2p\n      name: MTEB BiorxivClusteringP2P\n      config: default\n      split: test\n      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n    metrics:\n    - type: v_measure\n      value: 40.86793640310432\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/biorxiv-clustering-s2s\n      name: MTEB BiorxivClusteringS2S\n      config: default\n      split: test\n      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n    metrics:\n    - type: v_measure\n      value: 39.80291334130727\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackAndroidRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 38.421\n    - type: map_at_10\n      value: 52.349000000000004\n    - type: map_at_100\n      value: 52.349000000000004\n    - type: map_at_1000\n      value: 52.349000000000004\n    - type: map_at_3\n      value: 48.17\n    - type: map_at_5\n      value: 50.432\n    - type: mrr_at_1\n      value: 47.353\n    - type: mrr_at_10\n      value: 58.387\n    - type: mrr_at_100\n      value: 58.387\n    - type: mrr_at_1000\n      value: 58.387\n    - type: mrr_at_3\n      value: 56.199\n    - type: mrr_at_5\n      value: 57.487\n    - type: ndcg_at_1\n      value: 47.353\n    - type: ndcg_at_10\n      value: 59.202\n    - type: ndcg_at_100\n      value: 58.848\n    - type: ndcg_at_1000\n      value: 58.831999999999994\n    - type: ndcg_at_3\n      value: 54.112\n    - type: ndcg_at_5\n      value: 56.312\n    - type: precision_at_1\n      value: 47.353\n    - type: precision_at_10\n      value: 11.459\n    - type: precision_at_100\n      value: 1.146\n    - type: precision_at_1000\n      value: 0.11499999999999999\n    - type: precision_at_3\n      value: 26.133\n    - type: precision_at_5\n      value: 18.627\n    - type: recall_at_1\n      value: 38.421\n    - type: recall_at_10\n      value: 71.89\n    - type: recall_at_100\n      value: 71.89\n    - type: recall_at_1000\n      value: 71.89\n    - type: recall_at_3\n      value: 56.58\n    - type: recall_at_5\n      value: 63.125\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackEnglishRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 38.025999999999996\n    - type: map_at_10\n      value: 50.590999999999994\n    - type: map_at_100\n      value: 51.99700000000001\n    - type: map_at_1000\n      value: 52.11599999999999\n    - type: map_at_3\n      value: 47.435\n    - type: map_at_5\n      value: 49.236000000000004\n    - type: mrr_at_1\n      value: 48.28\n    - type: mrr_at_10\n      value: 56.814\n    - type: mrr_at_100\n      value: 57.446\n    - type: mrr_at_1000\n      value: 57.476000000000006\n    - type: mrr_at_3\n      value: 54.958\n    - type: mrr_at_5\n      value: 56.084999999999994\n    - type: ndcg_at_1\n      value: 48.28\n    - type: ndcg_at_10\n      value: 56.442\n    - type: ndcg_at_100\n      value: 60.651999999999994\n    - type: ndcg_at_1000\n      value: 62.187000000000005\n    - type: ndcg_at_3\n      value: 52.866\n    - type: ndcg_at_5\n      value: 54.515\n    - type: precision_at_1\n      value: 48.28\n    - type: precision_at_10\n      value: 10.586\n    - type: precision_at_100\n      value: 1.6310000000000002\n    - type: precision_at_1000\n      value: 0.20600000000000002\n    - type: precision_at_3\n      value: 25.945\n    - type: precision_at_5\n      value: 18.076\n    - type: recall_at_1\n      value: 38.025999999999996\n    - type: recall_at_10\n      value: 66.11399999999999\n    - type: recall_at_100\n      value: 83.339\n    - type: recall_at_1000\n      value: 92.413\n    - type: recall_at_3\n      value: 54.493\n    - type: recall_at_5\n      value: 59.64699999999999\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackGamingRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 47.905\n    - type: map_at_10\n      value: 61.58\n    - type: map_at_100\n      value: 62.605\n    - type: map_at_1000\n      value: 62.637\n    - type: map_at_3\n      value: 58.074000000000005\n    - type: map_at_5\n      value: 60.260000000000005\n    - type: mrr_at_1\n      value: 54.42\n    - type: mrr_at_10\n      value: 64.847\n    - type: mrr_at_100\n      value: 65.403\n    - type: mrr_at_1000\n      value: 65.41900000000001\n    - type: mrr_at_3\n      value: 62.675000000000004\n    - type: mrr_at_5\n      value: 64.101\n    - type: ndcg_at_1\n      value: 54.42\n    - type: ndcg_at_10\n      value: 67.394\n    - type: ndcg_at_100\n      value: 70.846\n    - type: ndcg_at_1000\n      value: 71.403\n    - type: ndcg_at_3\n      value: 62.025\n    - type: ndcg_at_5\n      value: 65.032\n    - type: precision_at_1\n      value: 54.42\n    - type: precision_at_10\n      value: 10.646\n    - type: precision_at_100\n      value: 1.325\n    - type: precision_at_1000\n      value: 0.13999999999999999\n    - type: precision_at_3\n      value: 27.398\n    - type: precision_at_5\n      value: 18.796\n    - type: recall_at_1\n      value: 47.905\n    - type: recall_at_10\n      value: 80.84599999999999\n    - type: recall_at_100\n      value: 95.078\n    - type: recall_at_1000\n      value: 98.878\n    - type: recall_at_3\n      value: 67.05600000000001\n    - type: recall_at_5\n      value: 74.261\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackGisRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 30.745\n    - type: map_at_10\n      value: 41.021\n    - type: map_at_100\n      value: 41.021\n    - type: map_at_1000\n      value: 41.021\n    - type: map_at_3\n      value: 37.714999999999996\n    - type: map_at_5\n      value: 39.766\n    - type: mrr_at_1\n      value: 33.559\n    - type: mrr_at_10\n      value: 43.537\n    - type: mrr_at_100\n      value: 43.537\n    - type: mrr_at_1000\n      value: 43.537\n    - type: mrr_at_3\n      value: 40.546\n    - type: mrr_at_5\n      value: 42.439\n    - type: ndcg_at_1\n      value: 33.559\n    - type: ndcg_at_10\n      value: 46.781\n    - type: ndcg_at_100\n      value: 46.781\n    - type: ndcg_at_1000\n      value: 46.781\n    - type: ndcg_at_3\n      value: 40.516000000000005\n    - type: ndcg_at_5\n      value: 43.957\n    - type: precision_at_1\n      value: 33.559\n    - type: precision_at_10\n      value: 7.198\n    - type: precision_at_100\n      value: 0.72\n    - type: precision_at_1000\n      value: 0.07200000000000001\n    - type: precision_at_3\n      value: 17.1\n    - type: precision_at_5\n      value: 12.316\n    - type: recall_at_1\n      value: 30.745\n    - type: recall_at_10\n      value: 62.038000000000004\n    - type: recall_at_100\n      value: 62.038000000000004\n    - type: recall_at_1000\n      value: 62.038000000000004\n    - type: recall_at_3\n      value: 45.378\n    - type: recall_at_5\n      value: 53.580000000000005\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackMathematicaRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 19.637999999999998\n    - type: map_at_10\n      value: 31.05\n    - type: map_at_100\n      value: 31.05\n    - type: map_at_1000\n      value: 31.05\n    - type: map_at_3\n      value: 27.628000000000004\n    - type: map_at_5\n      value: 29.767\n    - type: mrr_at_1\n      value: 25.0\n    - type: mrr_at_10\n      value: 36.131\n    - type: mrr_at_100\n      value: 36.131\n    - type: mrr_at_1000\n      value: 36.131\n    - type: mrr_at_3\n      value: 33.333\n    - type: mrr_at_5\n      value: 35.143\n    - type: ndcg_at_1\n      value: 25.0\n    - type: ndcg_at_10\n      value: 37.478\n    - type: ndcg_at_100\n      value: 37.469\n    - type: ndcg_at_1000\n      value: 37.469\n    - type: ndcg_at_3\n      value: 31.757999999999996\n    - type: ndcg_at_5\n      value: 34.821999999999996\n    - type: precision_at_1\n      value: 25.0\n    - type: precision_at_10\n      value: 7.188999999999999\n    - type: precision_at_100\n      value: 0.719\n    - type: precision_at_1000\n      value: 0.07200000000000001\n    - type: precision_at_3\n      value: 15.837000000000002\n    - type: precision_at_5\n      value: 11.841\n    - type: recall_at_1\n      value: 19.637999999999998\n    - type: recall_at_10\n      value: 51.836000000000006\n    - type: recall_at_100\n      value: 51.836000000000006\n    - type: recall_at_1000\n      value: 51.836000000000006\n    - type: recall_at_3\n      value: 36.384\n    - type: recall_at_5\n      value: 43.964\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackPhysicsRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 34.884\n    - type: map_at_10\n      value: 47.88\n    - type: map_at_100\n      value: 47.88\n    - type: map_at_1000\n      value: 47.88\n    - type: map_at_3\n      value: 43.85\n    - type: map_at_5\n      value: 46.414\n    - type: mrr_at_1\n      value: 43.022\n    - type: mrr_at_10\n      value: 53.569\n    - type: mrr_at_100\n      value: 53.569\n    - type: mrr_at_1000\n      value: 53.569\n    - type: mrr_at_3\n      value: 51.075\n    - type: mrr_at_5\n      value: 52.725\n    - type: ndcg_at_1\n      value: 43.022\n    - type: ndcg_at_10\n      value: 54.461000000000006\n    - type: ndcg_at_100\n      value: 54.388000000000005\n    - type: ndcg_at_1000\n      value: 54.388000000000005\n    - type: ndcg_at_3\n      value: 48.864999999999995\n    - type: ndcg_at_5\n      value: 52.032000000000004\n    - type: precision_at_1\n      value: 43.022\n    - type: precision_at_10\n      value: 9.885\n    - type: precision_at_100\n      value: 0.988\n    - type: precision_at_1000\n      value: 0.099\n    - type: precision_at_3\n      value: 23.612\n    - type: precision_at_5\n      value: 16.997\n    - type: recall_at_1\n      value: 34.884\n    - type: recall_at_10\n      value: 68.12899999999999\n    - type: recall_at_100\n      value: 68.12899999999999\n    - type: recall_at_1000\n      value: 68.12899999999999\n    - type: recall_at_3\n      value: 52.428\n    - type: recall_at_5\n      value: 60.662000000000006\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackProgrammersRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 31.588\n    - type: map_at_10\n      value: 43.85\n    - type: map_at_100\n      value: 45.317\n    - type: map_at_1000\n      value: 45.408\n    - type: map_at_3\n      value: 39.73\n    - type: map_at_5\n      value: 42.122\n    - type: mrr_at_1\n      value: 38.927\n    - type: mrr_at_10\n      value: 49.582\n    - type: mrr_at_100\n      value: 50.39\n    - type: mrr_at_1000\n      value: 50.426\n    - type: mrr_at_3\n      value: 46.518\n    - type: mrr_at_5\n      value: 48.271\n    - type: ndcg_at_1\n      value: 38.927\n    - type: ndcg_at_10\n      value: 50.605999999999995\n    - type: ndcg_at_100\n      value: 56.22200000000001\n    - type: ndcg_at_1000\n      value: 57.724\n    - type: ndcg_at_3\n      value: 44.232\n    - type: ndcg_at_5\n      value: 47.233999999999995\n    - type: precision_at_1\n      value: 38.927\n    - type: precision_at_10\n      value: 9.429\n    - type: precision_at_100\n      value: 1.435\n    - type: precision_at_1000\n      value: 0.172\n    - type: precision_at_3\n      value: 21.271\n    - type: precision_at_5\n      value: 15.434000000000001\n    - type: recall_at_1\n      value: 31.588\n    - type: recall_at_10\n      value: 64.836\n    - type: recall_at_100\n      value: 88.066\n    - type: recall_at_1000\n      value: 97.748\n    - type: recall_at_3\n      value: 47.128\n    - type: recall_at_5\n      value: 54.954\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 31.956083333333336\n    - type: map_at_10\n      value: 43.33483333333333\n    - type: map_at_100\n      value: 44.64883333333333\n    - type: map_at_1000\n      value: 44.75\n    - type: map_at_3\n      value: 39.87741666666666\n    - type: map_at_5\n      value: 41.86766666666667\n    - type: mrr_at_1\n      value: 38.06341666666667\n    - type: mrr_at_10\n      value: 47.839666666666666\n    - type: mrr_at_100\n      value: 48.644000000000005\n    - type: mrr_at_1000\n      value: 48.68566666666667\n    - type: mrr_at_3\n      value: 45.26358333333334\n    - type: mrr_at_5\n      value: 46.790000000000006\n    - type: ndcg_at_1\n      value: 38.06341666666667\n    - type: ndcg_at_10\n      value: 49.419333333333334\n    - type: ndcg_at_100\n      value: 54.50166666666667\n    - type: ndcg_at_1000\n      value: 56.161166666666674\n    - type: ndcg_at_3\n      value: 43.982416666666666\n    - type: ndcg_at_5\n      value: 46.638083333333334\n    - type: precision_at_1\n      value: 38.06341666666667\n    - type: precision_at_10\n      value: 8.70858333333333\n    - type: precision_at_100\n      value: 1.327\n    - type: precision_at_1000\n      value: 0.165\n    - type: precision_at_3\n      value: 20.37816666666667\n    - type: precision_at_5\n      value: 14.516333333333334\n    - type: recall_at_1\n      value: 31.956083333333336\n    - type: recall_at_10\n      value: 62.69458333333334\n    - type: recall_at_100\n      value: 84.46433333333334\n    - type: recall_at_1000\n      value: 95.58449999999999\n    - type: recall_at_3\n      value: 47.52016666666666\n    - type: recall_at_5\n      value: 54.36066666666666\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackStatsRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 28.912\n    - type: map_at_10\n      value: 38.291\n    - type: map_at_100\n      value: 39.44\n    - type: map_at_1000\n      value: 39.528\n    - type: map_at_3\n      value: 35.638\n    - type: map_at_5\n      value: 37.218\n    - type: mrr_at_1\n      value: 32.822\n    - type: mrr_at_10\n      value: 41.661\n    - type: mrr_at_100\n      value: 42.546\n    - type: mrr_at_1000\n      value: 42.603\n    - type: mrr_at_3\n      value: 39.238\n    - type: mrr_at_5\n      value: 40.726\n    - type: ndcg_at_1\n      value: 32.822\n    - type: ndcg_at_10\n      value: 43.373\n    - type: ndcg_at_100\n      value: 48.638\n    - type: ndcg_at_1000\n      value: 50.654999999999994\n    - type: ndcg_at_3\n      value: 38.643\n    - type: ndcg_at_5\n      value: 41.126000000000005\n    - type: precision_at_1\n      value: 32.822\n    - type: precision_at_10\n      value: 6.8709999999999996\n    - type: precision_at_100\n      value: 1.032\n    - type: precision_at_1000\n      value: 0.128\n    - type: precision_at_3\n      value: 16.82\n    - type: precision_at_5\n      value: 11.718\n    - type: recall_at_1\n      value: 28.912\n    - type: recall_at_10\n      value: 55.376999999999995\n    - type: recall_at_100\n      value: 79.066\n    - type: recall_at_1000\n      value: 93.664\n    - type: recall_at_3\n      value: 42.569\n    - type: recall_at_5\n      value: 48.719\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackTexRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 22.181\n    - type: map_at_10\n      value: 31.462\n    - type: map_at_100\n      value: 32.73\n    - type: map_at_1000\n      value: 32.848\n    - type: map_at_3\n      value: 28.57\n    - type: map_at_5\n      value: 30.182\n    - type: mrr_at_1\n      value: 27.185\n    - type: mrr_at_10\n      value: 35.846000000000004\n    - type: mrr_at_100\n      value: 36.811\n    - type: mrr_at_1000\n      value: 36.873\n    - type: mrr_at_3\n      value: 33.437\n    - type: mrr_at_5\n      value: 34.813\n    - type: ndcg_at_1\n      value: 27.185\n    - type: ndcg_at_10\n      value: 36.858000000000004\n    - type: ndcg_at_100\n      value: 42.501\n    - type: ndcg_at_1000\n      value: 44.945\n    - type: ndcg_at_3\n      value: 32.066\n    - type: ndcg_at_5\n      value: 34.29\n    - type: precision_at_1\n      value: 27.185\n    - type: precision_at_10\n      value: 6.752\n    - type: precision_at_100\n      value: 1.111\n    - type: precision_at_1000\n      value: 0.151\n    - type: precision_at_3\n      value: 15.290000000000001\n    - type: precision_at_5\n      value: 11.004999999999999\n    - type: recall_at_1\n      value: 22.181\n    - type: recall_at_10\n      value: 48.513\n    - type: recall_at_100\n      value: 73.418\n    - type: recall_at_1000\n      value: 90.306\n    - type: recall_at_3\n      value: 35.003\n    - type: recall_at_5\n      value: 40.876000000000005\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackUnixRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 33.934999999999995\n    - type: map_at_10\n      value: 44.727\n    - type: map_at_100\n      value: 44.727\n    - type: map_at_1000\n      value: 44.727\n    - type: map_at_3\n      value: 40.918\n    - type: map_at_5\n      value: 42.961\n    - type: mrr_at_1\n      value: 39.646\n    - type: mrr_at_10\n      value: 48.898\n    - type: mrr_at_100\n      value: 48.898\n    - type: mrr_at_1000\n      value: 48.898\n    - type: mrr_at_3\n      value: 45.896\n    - type: mrr_at_5\n      value: 47.514\n    - type: ndcg_at_1\n      value: 39.646\n    - type: ndcg_at_10\n      value: 50.817\n    - type: ndcg_at_100\n      value: 50.803\n    - type: ndcg_at_1000\n      value: 50.803\n    - type: ndcg_at_3\n      value: 44.507999999999996\n    - type: ndcg_at_5\n      value: 47.259\n    - type: precision_at_1\n      value: 39.646\n    - type: precision_at_10\n      value: 8.759\n    - type: precision_at_100\n      value: 0.876\n    - type: precision_at_1000\n      value: 0.08800000000000001\n    - type: precision_at_3\n      value: 20.274\n    - type: precision_at_5\n      value: 14.366000000000001\n    - type: recall_at_1\n      value: 33.934999999999995\n    - type: recall_at_10\n      value: 65.037\n    - type: recall_at_100\n      value: 65.037\n    - type: recall_at_1000\n      value: 65.037\n    - type: recall_at_3\n      value: 47.439\n    - type: recall_at_5\n      value: 54.567\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackWebmastersRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 32.058\n    - type: map_at_10\n      value: 43.137\n    - type: map_at_100\n      value: 43.137\n    - type: map_at_1000\n      value: 43.137\n    - type: map_at_3\n      value: 39.882\n    - type: map_at_5\n      value: 41.379\n    - type: mrr_at_1\n      value: 38.933\n    - type: mrr_at_10\n      value: 48.344\n    - type: mrr_at_100\n      value: 48.344\n    - type: mrr_at_1000\n      value: 48.344\n    - type: mrr_at_3\n      value: 45.652\n    - type: mrr_at_5\n      value: 46.877\n    - type: ndcg_at_1\n      value: 38.933\n    - type: ndcg_at_10\n      value: 49.964\n    - type: ndcg_at_100\n      value: 49.242000000000004\n    - type: ndcg_at_1000\n      value: 49.222\n    - type: ndcg_at_3\n      value: 44.605\n    - type: ndcg_at_5\n      value: 46.501999999999995\n    - type: precision_at_1\n      value: 38.933\n    - type: precision_at_10\n      value: 9.427000000000001\n    - type: precision_at_100\n      value: 0.943\n    - type: precision_at_1000\n      value: 0.094\n    - type: precision_at_3\n      value: 20.685000000000002\n    - type: precision_at_5\n      value: 14.585\n    - type: recall_at_1\n      value: 32.058\n    - type: recall_at_10\n      value: 63.074\n    - type: recall_at_100\n      value: 63.074\n    - type: recall_at_1000\n      value: 63.074\n    - type: recall_at_3\n      value: 47.509\n    - type: recall_at_5\n      value: 52.455\n  - task:\n      type: Retrieval\n    dataset:\n      type: BeIR/cqadupstack\n      name: MTEB CQADupstackWordpressRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 26.029000000000003\n    - type: map_at_10\n      value: 34.646\n    - type: map_at_100\n      value: 34.646\n    - type: map_at_1000\n      value: 34.646\n    - type: map_at_3\n      value: 31.456\n    - type: map_at_5\n      value: 33.138\n    - type: mrr_at_1\n      value: 28.281\n    - type: mrr_at_10\n      value: 36.905\n    - type: mrr_at_100\n      value: 36.905\n    - type: mrr_at_1000\n      value: 36.905\n    - type: mrr_at_3\n      value: 34.011\n    - type: mrr_at_5\n      value: 35.638\n    - type: ndcg_at_1\n      value: 28.281\n    - type: ndcg_at_10\n      value: 40.159\n    - type: ndcg_at_100\n      value: 40.159\n    - type: ndcg_at_1000\n      value: 40.159\n    - type: ndcg_at_3\n      value: 33.995\n    - type: ndcg_at_5\n      value: 36.836999999999996\n    - type: precision_at_1\n      value: 28.281\n    - type: precision_at_10\n      value: 6.358999999999999\n    - type: precision_at_100\n      value: 0.636\n    - type: precision_at_1000\n      value: 0.064\n    - type: precision_at_3\n      value: 14.233\n    - type: precision_at_5\n      value: 10.314\n    - type: recall_at_1\n      value: 26.029000000000003\n    - type: recall_at_10\n      value: 55.08\n    - type: recall_at_100\n      value: 55.08\n    - type: recall_at_1000\n      value: 55.08\n    - type: recall_at_3\n      value: 38.487\n    - type: recall_at_5\n      value: 45.308\n  - task:\n      type: Retrieval\n    dataset:\n      type: climate-fever\n      name: MTEB ClimateFEVER\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 12.842999999999998\n    - type: map_at_10\n      value: 22.101000000000003\n    - type: map_at_100\n      value: 24.319\n    - type: map_at_1000\n      value: 24.51\n    - type: map_at_3\n      value: 18.372\n    - type: map_at_5\n      value: 20.323\n    - type: mrr_at_1\n      value: 27.948\n    - type: mrr_at_10\n      value: 40.321\n    - type: mrr_at_100\n      value: 41.262\n    - type: mrr_at_1000\n      value: 41.297\n    - type: mrr_at_3\n      value: 36.558\n    - type: mrr_at_5\n      value: 38.824999999999996\n    - type: ndcg_at_1\n      value: 27.948\n    - type: ndcg_at_10\n      value: 30.906\n    - type: ndcg_at_100\n      value: 38.986\n    - type: ndcg_at_1000\n      value: 42.136\n    - type: ndcg_at_3\n      value: 24.911\n    - type: ndcg_at_5\n      value: 27.168999999999997\n    - type: precision_at_1\n      value: 27.948\n    - type: precision_at_10\n      value: 9.798\n    - type: precision_at_100\n      value: 1.8399999999999999\n    - type: precision_at_1000\n      value: 0.243\n    - type: precision_at_3\n      value: 18.328\n    - type: precision_at_5\n      value: 14.502\n    - type: recall_at_1\n      value: 12.842999999999998\n    - type: recall_at_10\n      value: 37.245\n    - type: recall_at_100\n      value: 64.769\n    - type: recall_at_1000\n      value: 82.055\n    - type: recall_at_3\n      value: 23.159\n    - type: recall_at_5\n      value: 29.113\n  - task:\n      type: Retrieval\n    dataset:\n      type: dbpedia-entity\n      name: MTEB DBPedia\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 8.934000000000001\n    - type: map_at_10\n      value: 21.915000000000003\n    - type: map_at_100\n      value: 21.915000000000003\n    - type: map_at_1000\n      value: 21.915000000000003\n    - type: map_at_3\n      value: 14.623\n    - type: map_at_5\n      value: 17.841\n    - type: mrr_at_1\n      value: 71.25\n    - type: mrr_at_10\n      value: 78.994\n    - type: mrr_at_100\n      value: 78.994\n    - type: mrr_at_1000\n      value: 78.994\n    - type: mrr_at_3\n      value: 77.208\n    - type: mrr_at_5\n      value: 78.55799999999999\n    - type: ndcg_at_1\n      value: 60.62499999999999\n    - type: ndcg_at_10\n      value: 46.604\n    - type: ndcg_at_100\n      value: 35.653\n    - type: ndcg_at_1000\n      value: 35.531\n    - type: ndcg_at_3\n      value: 50.605\n    - type: ndcg_at_5\n      value: 48.730000000000004\n    - type: precision_at_1\n      value: 71.25\n    - type: precision_at_10\n      value: 37.75\n    - type: precision_at_100\n      value: 3.775\n    - type: precision_at_1000\n      value: 0.377\n    - type: precision_at_3\n      value: 54.417\n    - type: precision_at_5\n      value: 48.15\n    - type: recall_at_1\n      value: 8.934000000000001\n    - type: recall_at_10\n      value: 28.471000000000004\n    - type: recall_at_100\n      value: 28.471000000000004\n    - type: recall_at_1000\n      value: 28.471000000000004\n    - type: recall_at_3\n      value: 16.019\n    - type: recall_at_5\n      value: 21.410999999999998\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/emotion\n      name: MTEB EmotionClassification\n      config: default\n      split: test\n      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n    metrics:\n    - type: accuracy\n      value: 52.81\n    - type: f1\n      value: 47.987573380720114\n  - task:\n      type: Retrieval\n    dataset:\n      type: fever\n      name: MTEB FEVER\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 66.81899999999999\n    - type: map_at_10\n      value: 78.034\n    - type: map_at_100\n      value: 78.034\n    - type: map_at_1000\n      value: 78.034\n    - type: map_at_3\n      value: 76.43100000000001\n    - type: map_at_5\n      value: 77.515\n    - type: mrr_at_1\n      value: 71.542\n    - type: mrr_at_10\n      value: 81.638\n    - type: mrr_at_100\n      value: 81.638\n    - type: mrr_at_1000\n      value: 81.638\n    - type: mrr_at_3\n      value: 80.403\n    - type: mrr_at_5\n      value: 81.256\n    - type: ndcg_at_1\n      value: 71.542\n    - type: ndcg_at_10\n      value: 82.742\n    - type: ndcg_at_100\n      value: 82.741\n    - type: ndcg_at_1000\n      value: 82.741\n    - type: ndcg_at_3\n      value: 80.039\n    - type: ndcg_at_5\n      value: 81.695\n    - type: precision_at_1\n      value: 71.542\n    - type: precision_at_10\n      value: 10.387\n    - type: precision_at_100\n      value: 1.039\n    - type: precision_at_1000\n      value: 0.104\n    - type: precision_at_3\n      value: 31.447999999999997\n    - type: precision_at_5\n      value: 19.91\n    - type: recall_at_1\n      value: 66.81899999999999\n    - type: recall_at_10\n      value: 93.372\n    - type: recall_at_100\n      value: 93.372\n    - type: recall_at_1000\n      value: 93.372\n    - type: recall_at_3\n      value: 86.33\n    - type: recall_at_5\n      value: 90.347\n  - task:\n      type: Retrieval\n    dataset:\n      type: fiqa\n      name: MTEB FiQA2018\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 31.158\n    - type: map_at_10\n      value: 52.017\n    - type: map_at_100\n      value: 54.259\n    - type: map_at_1000\n      value: 54.367\n    - type: map_at_3\n      value: 45.738\n    - type: map_at_5\n      value: 49.283\n    - type: mrr_at_1\n      value: 57.87\n    - type: mrr_at_10\n      value: 66.215\n    - type: mrr_at_100\n      value: 66.735\n    - type: mrr_at_1000\n      value: 66.75\n    - type: mrr_at_3\n      value: 64.043\n    - type: mrr_at_5\n      value: 65.116\n    - type: ndcg_at_1\n      value: 57.87\n    - type: ndcg_at_10\n      value: 59.946999999999996\n    - type: ndcg_at_100\n      value: 66.31099999999999\n    - type: ndcg_at_1000\n      value: 67.75999999999999\n    - type: ndcg_at_3\n      value: 55.483000000000004\n    - type: ndcg_at_5\n      value: 56.891000000000005\n    - type: precision_at_1\n      value: 57.87\n    - type: precision_at_10\n      value: 16.497\n    - type: precision_at_100\n      value: 2.321\n    - type: precision_at_1000\n      value: 0.258\n    - type: precision_at_3\n      value: 37.14\n    - type: precision_at_5\n      value: 27.067999999999998\n    - type: recall_at_1\n      value: 31.158\n    - type: recall_at_10\n      value: 67.381\n    - type: recall_at_100\n      value: 89.464\n    - type: recall_at_1000\n      value: 97.989\n    - type: recall_at_3\n      value: 50.553000000000004\n    - type: recall_at_5\n      value: 57.824\n  - task:\n      type: Retrieval\n    dataset:\n      type: hotpotqa\n      name: MTEB HotpotQA\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 42.073\n    - type: map_at_10\n      value: 72.418\n    - type: map_at_100\n      value: 73.175\n    - type: map_at_1000\n      value: 73.215\n    - type: map_at_3\n      value: 68.791\n    - type: map_at_5\n      value: 71.19\n    - type: mrr_at_1\n      value: 84.146\n    - type: mrr_at_10\n      value: 88.994\n    - type: mrr_at_100\n      value: 89.116\n    - type: mrr_at_1000\n      value: 89.12\n    - type: mrr_at_3\n      value: 88.373\n    - type: mrr_at_5\n      value: 88.82\n    - type: ndcg_at_1\n      value: 84.146\n    - type: ndcg_at_10\n      value: 79.404\n    - type: ndcg_at_100\n      value: 81.83200000000001\n    - type: ndcg_at_1000\n      value: 82.524\n    - type: ndcg_at_3\n      value: 74.595\n    - type: ndcg_at_5\n      value: 77.474\n    - type: precision_at_1\n      value: 84.146\n    - type: precision_at_10\n      value: 16.753999999999998\n    - type: precision_at_100\n      value: 1.8599999999999999\n    - type: precision_at_1000\n      value: 0.19499999999999998\n    - type: precision_at_3\n      value: 48.854\n    - type: precision_at_5\n      value: 31.579\n    - type: recall_at_1\n      value: 42.073\n    - type: recall_at_10\n      value: 83.768\n    - type: recall_at_100\n      value: 93.018\n    - type: recall_at_1000\n      value: 97.481\n    - type: recall_at_3\n      value: 73.282\n    - type: recall_at_5\n      value: 78.947\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/imdb\n      name: MTEB ImdbClassification\n      config: default\n      split: test\n      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n    metrics:\n    - type: accuracy\n      value: 94.9968\n    - type: ap\n      value: 92.93892195862824\n    - type: f1\n      value: 94.99327998213761\n  - task:\n      type: Retrieval\n    dataset:\n      type: msmarco\n      name: MTEB MSMARCO\n      config: default\n      split: dev\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 21.698\n    - type: map_at_10\n      value: 34.585\n    - type: map_at_100\n      value: 35.782000000000004\n    - type: map_at_1000\n      value: 35.825\n    - type: map_at_3\n      value: 30.397999999999996\n    - type: map_at_5\n      value: 32.72\n    - type: mrr_at_1\n      value: 22.192\n    - type: mrr_at_10\n      value: 35.085\n    - type: mrr_at_100\n      value: 36.218\n    - type: mrr_at_1000\n      value: 36.256\n    - type: mrr_at_3\n      value: 30.986000000000004\n    - type: mrr_at_5\n      value: 33.268\n    - type: ndcg_at_1\n      value: 22.192\n    - type: ndcg_at_10\n      value: 41.957\n    - type: ndcg_at_100\n      value: 47.658\n    - type: ndcg_at_1000\n      value: 48.697\n    - type: ndcg_at_3\n      value: 33.433\n    - type: ndcg_at_5\n      value: 37.551\n    - type: precision_at_1\n      value: 22.192\n    - type: precision_at_10\n      value: 6.781\n    - type: precision_at_100\n      value: 0.963\n    - type: precision_at_1000\n      value: 0.105\n    - type: precision_at_3\n      value: 14.365\n    - type: precision_at_5\n      value: 10.713000000000001\n    - type: recall_at_1\n      value: 21.698\n    - type: recall_at_10\n      value: 64.79\n    - type: recall_at_100\n      value: 91.071\n    - type: recall_at_1000\n      value: 98.883\n    - type: recall_at_3\n      value: 41.611\n    - type: recall_at_5\n      value: 51.459999999999994\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/mtop_domain\n      name: MTEB MTOPDomainClassification (en)\n      config: en\n      split: test\n      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n    metrics:\n    - type: accuracy\n      value: 96.15823073415413\n    - type: f1\n      value: 96.00362034963248\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/mtop_intent\n      name: MTEB MTOPIntentClassification (en)\n      config: en\n      split: test\n      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n    metrics:\n    - type: accuracy\n      value: 87.12722298221614\n    - type: f1\n      value: 70.46888967516227\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/amazon_massive_intent\n      name: MTEB MassiveIntentClassification (en)\n      config: en\n      split: test\n      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n    metrics:\n    - type: accuracy\n      value: 80.77673167451245\n    - type: f1\n      value: 77.60202561132175\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/amazon_massive_scenario\n      name: MTEB MassiveScenarioClassification (en)\n      config: en\n      split: test\n      revision: 7d571f92784cd94a019292a1f45445077d0ef634\n    metrics:\n    - type: accuracy\n      value: 82.09145931405514\n    - type: f1\n      value: 81.7701921473406\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/medrxiv-clustering-p2p\n      name: MTEB MedrxivClusteringP2P\n      config: default\n      split: test\n      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n    metrics:\n    - type: v_measure\n      value: 36.52153488185864\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/medrxiv-clustering-s2s\n      name: MTEB MedrxivClusteringS2S\n      config: default\n      split: test\n      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n    metrics:\n    - type: v_measure\n      value: 36.80090398444147\n  - task:\n      type: Reranking\n    dataset:\n      type: mteb/mind_small\n      name: MTEB MindSmallReranking\n      config: default\n      split: test\n      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n    metrics:\n    - type: map\n      value: 31.807141746058605\n    - type: mrr\n      value: 32.85025611455029\n  - task:\n      type: Retrieval\n    dataset:\n      type: nfcorpus\n      name: MTEB NFCorpus\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 6.920999999999999\n    - type: map_at_10\n      value: 16.049\n    - type: map_at_100\n      value: 16.049\n    - type: map_at_1000\n      value: 16.049\n    - type: map_at_3\n      value: 11.865\n    - type: map_at_5\n      value: 13.657\n    - type: mrr_at_1\n      value: 53.87\n    - type: mrr_at_10\n      value: 62.291\n    - type: mrr_at_100\n      value: 62.291\n    - type: mrr_at_1000\n      value: 62.291\n    - type: mrr_at_3\n      value: 60.681\n    - type: mrr_at_5\n      value: 61.61\n    - type: ndcg_at_1\n      value: 51.23799999999999\n    - type: ndcg_at_10\n      value: 40.892\n    - type: ndcg_at_100\n      value: 26.951999999999998\n    - type: ndcg_at_1000\n      value: 26.474999999999998\n    - type: ndcg_at_3\n      value: 46.821\n    - type: ndcg_at_5\n      value: 44.333\n    - type: precision_at_1\n      value: 53.251000000000005\n    - type: precision_at_10\n      value: 30.124000000000002\n    - type: precision_at_100\n      value: 3.012\n    - type: precision_at_1000\n      value: 0.301\n    - type: precision_at_3\n      value: 43.55\n    - type: precision_at_5\n      value: 38.266\n    - type: recall_at_1\n      value: 6.920999999999999\n    - type: recall_at_10\n      value: 20.852\n    - type: recall_at_100\n      value: 20.852\n    - type: recall_at_1000\n      value: 20.852\n    - type: recall_at_3\n      value: 13.628000000000002\n    - type: recall_at_5\n      value: 16.273\n  - task:\n      type: Retrieval\n    dataset:\n      type: nq\n      name: MTEB NQ\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 46.827999999999996\n    - type: map_at_10\n      value: 63.434000000000005\n    - type: map_at_100\n      value: 63.434000000000005\n    - type: map_at_1000\n      value: 63.434000000000005\n    - type: map_at_3\n      value: 59.794000000000004\n    - type: map_at_5\n      value: 62.08\n    - type: mrr_at_1\n      value: 52.288999999999994\n    - type: mrr_at_10\n      value: 65.95\n    - type: mrr_at_100\n      value: 65.95\n    - type: mrr_at_1000\n      value: 65.95\n    - type: mrr_at_3\n      value: 63.413\n    - type: mrr_at_5\n      value: 65.08\n    - type: ndcg_at_1\n      value: 52.288999999999994\n    - type: ndcg_at_10\n      value: 70.301\n    - type: ndcg_at_100\n      value: 70.301\n    - type: ndcg_at_1000\n      value: 70.301\n    - type: ndcg_at_3\n      value: 63.979\n    - type: ndcg_at_5\n      value: 67.582\n    - type: precision_at_1\n      value: 52.288999999999994\n    - type: precision_at_10\n      value: 10.576\n    - type: precision_at_100\n      value: 1.058\n    - type: precision_at_1000\n      value: 0.106\n    - type: precision_at_3\n      value: 28.177000000000003\n    - type: precision_at_5\n      value: 19.073\n    - type: recall_at_1\n      value: 46.827999999999996\n    - type: recall_at_10\n      value: 88.236\n    - type: recall_at_100\n      value: 88.236\n    - type: recall_at_1000\n      value: 88.236\n    - type: recall_at_3\n      value: 72.371\n    - type: recall_at_5\n      value: 80.56\n  - task:\n      type: Retrieval\n    dataset:\n      type: quora\n      name: MTEB QuoraRetrieval\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 71.652\n    - type: map_at_10\n      value: 85.953\n    - type: map_at_100\n      value: 85.953\n    - type: map_at_1000\n      value: 85.953\n    - type: map_at_3\n      value: 83.05399999999999\n    - type: map_at_5\n      value: 84.89\n    - type: mrr_at_1\n      value: 82.42\n    - type: mrr_at_10\n      value: 88.473\n    - type: mrr_at_100\n      value: 88.473\n    - type: mrr_at_1000\n      value: 88.473\n    - type: mrr_at_3\n      value: 87.592\n    - type: mrr_at_5\n      value: 88.211\n    - type: ndcg_at_1\n      value: 82.44\n    - type: ndcg_at_10\n      value: 89.467\n    - type: ndcg_at_100\n      value: 89.33\n    - type: ndcg_at_1000\n      value: 89.33\n    - type: ndcg_at_3\n      value: 86.822\n    - type: ndcg_at_5\n      value: 88.307\n    - type: precision_at_1\n      value: 82.44\n    - type: precision_at_10\n      value: 13.616\n    - type: precision_at_100\n      value: 1.362\n    - type: precision_at_1000\n      value: 0.136\n    - type: precision_at_3\n      value: 38.117000000000004\n    - type: precision_at_5\n      value: 25.05\n    - type: recall_at_1\n      value: 71.652\n    - type: recall_at_10\n      value: 96.224\n    - type: recall_at_100\n      value: 96.224\n    - type: recall_at_1000\n      value: 96.224\n    - type: recall_at_3\n      value: 88.571\n    - type: recall_at_5\n      value: 92.812\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/reddit-clustering\n      name: MTEB RedditClustering\n      config: default\n      split: test\n      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n    metrics:\n    - type: v_measure\n      value: 61.295010338050474\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/reddit-clustering-p2p\n      name: MTEB RedditClusteringP2P\n      config: default\n      split: test\n      revision: 282350215ef01743dc01b456c7f5241fa8937f16\n    metrics:\n    - type: v_measure\n      value: 67.26380819328142\n  - task:\n      type: Retrieval\n    dataset:\n      type: scidocs\n      name: MTEB SCIDOCS\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 5.683\n    - type: map_at_10\n      value: 14.924999999999999\n    - type: map_at_100\n      value: 17.532\n    - type: map_at_1000\n      value: 17.875\n    - type: map_at_3\n      value: 10.392\n    - type: map_at_5\n      value: 12.592\n    - type: mrr_at_1\n      value: 28.000000000000004\n    - type: mrr_at_10\n      value: 39.951\n    - type: mrr_at_100\n      value: 41.025\n    - type: mrr_at_1000\n      value: 41.056\n    - type: mrr_at_3\n      value: 36.317\n    - type: mrr_at_5\n      value: 38.412\n    - type: ndcg_at_1\n      value: 28.000000000000004\n    - type: ndcg_at_10\n      value: 24.410999999999998\n    - type: ndcg_at_100\n      value: 33.79\n    - type: ndcg_at_1000\n      value: 39.035\n    - type: ndcg_at_3\n      value: 22.845\n    - type: ndcg_at_5\n      value: 20.080000000000002\n    - type: precision_at_1\n      value: 28.000000000000004\n    - type: precision_at_10\n      value: 12.790000000000001\n    - type: precision_at_100\n      value: 2.633\n    - type: precision_at_1000\n      value: 0.388\n    - type: precision_at_3\n      value: 21.367\n    - type: precision_at_5\n      value: 17.7\n    - type: recall_at_1\n      value: 5.683\n    - type: recall_at_10\n      value: 25.91\n    - type: recall_at_100\n      value: 53.443\n    - type: recall_at_1000\n      value: 78.73\n    - type: recall_at_3\n      value: 13.003\n    - type: recall_at_5\n      value: 17.932000000000002\n  - task:\n      type: STS\n    dataset:\n      type: mteb/sickr-sts\n      name: MTEB SICK-R\n      config: default\n      split: test\n      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n    metrics:\n    - type: cos_sim_pearson\n      value: 84.677978681023\n    - type: cos_sim_spearman\n      value: 83.13093441058189\n    - type: euclidean_pearson\n      value: 83.35535759341572\n    - type: euclidean_spearman\n      value: 83.42583744219611\n    - type: manhattan_pearson\n      value: 83.2243124045889\n    - type: manhattan_spearman\n      value: 83.39801618652632\n  - task:\n      type: STS\n    dataset:\n      type: mteb/sts12-sts\n      name: MTEB STS12\n      config: default\n      split: test\n      revision: a0d554a64d88156834ff5ae9920b964011b16384\n    metrics:\n    - type: cos_sim_pearson\n      value: 81.68960206569666\n    - type: cos_sim_spearman\n      value: 77.3368966488535\n    - type: euclidean_pearson\n      value: 77.62828980560303\n    - type: euclidean_spearman\n      value: 76.77951481444651\n    - type: manhattan_pearson\n      value: 77.88637240839041\n    - type: manhattan_spearman\n      value: 77.22157841466188\n  - task:\n      type: STS\n    dataset:\n      type: mteb/sts13-sts\n      name: MTEB STS13\n      config: default\n      split: test\n      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n    metrics:\n    - type: cos_sim_pearson\n      value: 84.18745821650724\n    - type: cos_sim_spearman\n      value: 85.04423285574542\n    - type: euclidean_pearson\n      value: 85.46604816931023\n    - type: euclidean_spearman\n      value: 85.5230593932974\n    - type: manhattan_pearson\n      value: 85.57912805986261\n    - type: manhattan_spearman\n      value: 85.65955905111873\n  - task:\n      type: STS\n    dataset:\n      type: mteb/sts14-sts\n      name: MTEB STS14\n      config: default\n      split: test\n      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n    metrics:\n    - type: cos_sim_pearson\n      value: 83.6715333300355\n    - type: cos_sim_spearman\n      value: 82.9058522514908\n    - type: euclidean_pearson\n      value: 83.9640357424214\n    - type: euclidean_spearman\n      value: 83.60415457472637\n    - type: manhattan_pearson\n      value: 84.05621005853469\n    - type: manhattan_spearman\n      value: 83.87077724707746\n  - task:\n      type: STS\n    dataset:\n      type: mteb/sts15-sts\n      name: MTEB STS15\n      config: default\n      split: test\n      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n    metrics:\n    - type: cos_sim_pearson\n      value: 87.82422928098886\n    - type: cos_sim_spearman\n      value: 88.12660311894628\n    - type: euclidean_pearson\n      value: 87.50974805056555\n    - type: euclidean_spearman\n      value: 87.91957275596677\n    - type: manhattan_pearson\n      value: 87.74119404878883\n    - type: manhattan_spearman\n      value: 88.2808922165719\n  - task:\n      type: STS\n    dataset:\n      type: mteb/sts16-sts\n      name: MTEB STS16\n      config: default\n      split: test\n      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n    metrics:\n    - type: cos_sim_pearson\n      value: 84.80605838552093\n    - type: cos_sim_spearman\n      value: 86.24123388765678\n    - type: euclidean_pearson\n      value: 85.32648347339814\n    - type: euclidean_spearman\n      value: 85.60046671950158\n    - type: manhattan_pearson\n      value: 85.53800168487811\n    - type: manhattan_spearman\n      value: 85.89542420480763\n  - task:\n      type: STS\n    dataset:\n      type: mteb/sts17-crosslingual-sts\n      name: MTEB STS17 (en-en)\n      config: en-en\n      split: test\n      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n    metrics:\n    - type: cos_sim_pearson\n      value: 89.87540978988132\n    - type: cos_sim_spearman\n      value: 90.12715295099461\n    - type: euclidean_pearson\n      value: 91.61085993525275\n    - type: euclidean_spearman\n      value: 91.31835942311758\n    - type: manhattan_pearson\n      value: 91.57500202032934\n    - type: manhattan_spearman\n      value: 91.1790925526635\n  - task:\n      type: STS\n    dataset:\n      type: mteb/sts22-crosslingual-sts\n      name: MTEB STS22 (en)\n      config: en\n      split: test\n      revision: eea2b4fe26a775864c896887d910b76a8098ad3f\n    metrics:\n    - type: cos_sim_pearson\n      value: 69.87136205329556\n    - type: cos_sim_spearman\n      value: 68.6253154635078\n    - type: euclidean_pearson\n      value: 68.91536015034222\n    - type: euclidean_spearman\n      value: 67.63744649352542\n    - type: manhattan_pearson\n      value: 69.2000713045275\n    - type: manhattan_spearman\n      value: 68.16002901587316\n  - task:\n      type: STS\n    dataset:\n      type: mteb/stsbenchmark-sts\n      name: MTEB STSBenchmark\n      config: default\n      split: test\n      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n    metrics:\n    - type: cos_sim_pearson\n      value: 85.21849551039082\n    - type: cos_sim_spearman\n      value: 85.6392959372461\n    - type: euclidean_pearson\n      value: 85.92050852609488\n    - type: euclidean_spearman\n      value: 85.97205649009734\n    - type: manhattan_pearson\n      value: 86.1031154802254\n    - type: manhattan_spearman\n      value: 86.26791155517466\n  - task:\n      type: Reranking\n    dataset:\n      type: mteb/scidocs-reranking\n      name: MTEB SciDocsRR\n      config: default\n      split: test\n      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n    metrics:\n    - type: map\n      value: 86.83953958636627\n    - type: mrr\n      value: 96.71167612344082\n  - task:\n      type: Retrieval\n    dataset:\n      type: scifact\n      name: MTEB SciFact\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 64.994\n    - type: map_at_10\n      value: 74.763\n    - type: map_at_100\n      value: 75.127\n    - type: map_at_1000\n      value: 75.143\n    - type: map_at_3\n      value: 71.824\n    - type: map_at_5\n      value: 73.71\n    - type: mrr_at_1\n      value: 68.333\n    - type: mrr_at_10\n      value: 75.749\n    - type: mrr_at_100\n      value: 75.922\n    - type: mrr_at_1000\n      value: 75.938\n    - type: mrr_at_3\n      value: 73.556\n    - type: mrr_at_5\n      value: 74.739\n    - type: ndcg_at_1\n      value: 68.333\n    - type: ndcg_at_10\n      value: 79.174\n    - type: ndcg_at_100\n      value: 80.41\n    - type: ndcg_at_1000\n      value: 80.804\n    - type: ndcg_at_3\n      value: 74.361\n    - type: ndcg_at_5\n      value: 76.861\n    - type: precision_at_1\n      value: 68.333\n    - type: precision_at_10\n      value: 10.333\n    - type: precision_at_100\n      value: 1.0999999999999999\n    - type: precision_at_1000\n      value: 0.11299999999999999\n    - type: precision_at_3\n      value: 28.778\n    - type: precision_at_5\n      value: 19.067\n    - type: recall_at_1\n      value: 64.994\n    - type: recall_at_10\n      value: 91.822\n    - type: recall_at_100\n      value: 97.0\n    - type: recall_at_1000\n      value: 100.0\n    - type: recall_at_3\n      value: 78.878\n    - type: recall_at_5\n      value: 85.172\n  - task:\n      type: PairClassification\n    dataset:\n      type: mteb/sprintduplicatequestions-pairclassification\n      name: MTEB SprintDuplicateQuestions\n      config: default\n      split: test\n      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n    metrics:\n    - type: cos_sim_accuracy\n      value: 99.72079207920792\n    - type: cos_sim_ap\n      value: 93.00265215525152\n    - type: cos_sim_f1\n      value: 85.06596306068602\n    - type: cos_sim_precision\n      value: 90.05586592178771\n    - type: cos_sim_recall\n      value: 80.60000000000001\n    - type: dot_accuracy\n      value: 99.66039603960397\n    - type: dot_ap\n      value: 91.22371407479089\n    - type: dot_f1\n      value: 82.34693877551021\n    - type: dot_precision\n      value: 84.0625\n    - type: dot_recall\n      value: 80.7\n    - type: euclidean_accuracy\n      value: 99.71881188118812\n    - type: euclidean_ap\n      value: 92.88449963304728\n    - type: euclidean_f1\n      value: 85.19480519480518\n    - type: euclidean_precision\n      value: 88.64864864864866\n    - type: euclidean_recall\n      value: 82.0\n    - type: manhattan_accuracy\n      value: 99.73267326732673\n    - type: manhattan_ap\n      value: 93.23055393056883\n    - type: manhattan_f1\n      value: 85.88957055214725\n    - type: manhattan_precision\n      value: 87.86610878661088\n    - type: manhattan_recall\n      value: 84.0\n    - type: max_accuracy\n      value: 99.73267326732673\n    - type: max_ap\n      value: 93.23055393056883\n    - type: max_f1\n      value: 85.88957055214725\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/stackexchange-clustering\n      name: MTEB StackExchangeClustering\n      config: default\n      split: test\n      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n    metrics:\n    - type: v_measure\n      value: 77.3305735900358\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/stackexchange-clustering-p2p\n      name: MTEB StackExchangeClusteringP2P\n      config: default\n      split: test\n      revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n    metrics:\n    - type: v_measure\n      value: 41.32967136540674\n  - task:\n      type: Reranking\n    dataset:\n      type: mteb/stackoverflowdupquestions-reranking\n      name: MTEB StackOverflowDupQuestions\n      config: default\n      split: test\n      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n    metrics:\n    - type: map\n      value: 55.95514866379359\n    - type: mrr\n      value: 56.95423245055598\n  - task:\n      type: Summarization\n    dataset:\n      type: mteb/summeval\n      name: MTEB SummEval\n      config: default\n      split: test\n      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n    metrics:\n    - type: cos_sim_pearson\n      value: 30.783007208997144\n    - type: cos_sim_spearman\n      value: 30.373444721540533\n    - type: dot_pearson\n      value: 29.210604111143905\n    - type: dot_spearman\n      value: 29.98809758085659\n  - task:\n      type: Retrieval\n    dataset:\n      type: trec-covid\n      name: MTEB TRECCOVID\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 0.234\n    - type: map_at_10\n      value: 1.894\n    - type: map_at_100\n      value: 1.894\n    - type: map_at_1000\n      value: 1.894\n    - type: map_at_3\n      value: 0.636\n    - type: map_at_5\n      value: 1.0\n    - type: mrr_at_1\n      value: 88.0\n    - type: mrr_at_10\n      value: 93.667\n    - type: mrr_at_100\n      value: 93.667\n    - type: mrr_at_1000\n      value: 93.667\n    - type: mrr_at_3\n      value: 93.667\n    - type: mrr_at_5\n      value: 93.667\n    - type: ndcg_at_1\n      value: 85.0\n    - type: ndcg_at_10\n      value: 74.798\n    - type: ndcg_at_100\n      value: 16.462\n    - type: ndcg_at_1000\n      value: 7.0889999999999995\n    - type: ndcg_at_3\n      value: 80.754\n    - type: ndcg_at_5\n      value: 77.319\n    - type: precision_at_1\n      value: 88.0\n    - type: precision_at_10\n      value: 78.0\n    - type: precision_at_100\n      value: 7.8\n    - type: precision_at_1000\n      value: 0.7799999999999999\n    - type: precision_at_3\n      value: 83.333\n    - type: precision_at_5\n      value: 80.80000000000001\n    - type: recall_at_1\n      value: 0.234\n    - type: recall_at_10\n      value: 2.093\n    - type: recall_at_100\n      value: 2.093\n    - type: recall_at_1000\n      value: 2.093\n    - type: recall_at_3\n      value: 0.662\n    - type: recall_at_5\n      value: 1.0739999999999998\n  - task:\n      type: Retrieval\n    dataset:\n      type: webis-touche2020\n      name: MTEB Touche2020\n      config: default\n      split: test\n      revision: None\n    metrics:\n    - type: map_at_1\n      value: 2.703\n    - type: map_at_10\n      value: 10.866000000000001\n    - type: map_at_100\n      value: 10.866000000000001\n    - type: map_at_1000\n      value: 10.866000000000001\n    - type: map_at_3\n      value: 5.909\n    - type: map_at_5\n      value: 7.35\n    - type: mrr_at_1\n      value: 36.735\n    - type: mrr_at_10\n      value: 53.583000000000006\n    - type: mrr_at_100\n      value: 53.583000000000006\n    - type: mrr_at_1000\n      value: 53.583000000000006\n    - type: mrr_at_3\n      value: 49.32\n    - type: mrr_at_5\n      value: 51.769\n    - type: ndcg_at_1\n      value: 34.694\n    - type: ndcg_at_10\n      value: 27.926000000000002\n    - type: ndcg_at_100\n      value: 22.701\n    - type: ndcg_at_1000\n      value: 22.701\n    - type: ndcg_at_3\n      value: 32.073\n    - type: ndcg_at_5\n      value: 28.327999999999996\n    - type: precision_at_1\n      value: 36.735\n    - type: precision_at_10\n      value: 24.694\n    - type: precision_at_100\n      value: 2.469\n    - type: precision_at_1000\n      value: 0.247\n    - type: precision_at_3\n      value: 31.973000000000003\n    - type: precision_at_5\n      value: 26.939\n    - type: recall_at_1\n      value: 2.703\n    - type: recall_at_10\n      value: 17.702\n    - type: recall_at_100\n      value: 17.702\n    - type: recall_at_1000\n      value: 17.702\n    - type: recall_at_3\n      value: 7.208\n    - type: recall_at_5\n      value: 9.748999999999999\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/toxic_conversations_50k\n      name: MTEB ToxicConversationsClassification\n      config: default\n      split: test\n      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n    metrics:\n    - type: accuracy\n      value: 70.79960000000001\n    - type: ap\n      value: 15.467565415565815\n    - type: f1\n      value: 55.28639823443618\n  - task:\n      type: Classification\n    dataset:\n      type: mteb/tweet_sentiment_extraction\n      name: MTEB TweetSentimentExtractionClassification\n      config: default\n      split: test\n      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n    metrics:\n    - type: accuracy\n      value: 64.7792869269949\n    - type: f1\n      value: 65.08597154774318\n  - task:\n      type: Clustering\n    dataset:\n      type: mteb/twentynewsgroups-clustering\n      name: MTEB TwentyNewsgroupsClustering\n      config: default\n      split: test\n      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n    metrics:\n    - type: v_measure\n      value: 55.70352297774293\n  - task:\n      type: PairClassification\n    dataset:\n      type: mteb/twittersemeval2015-pairclassification\n      name: MTEB TwitterSemEval2015\n      config: default\n      split: test\n      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n    metrics:\n    - type: cos_sim_accuracy\n      value: 88.27561542588067\n    - type: cos_sim_ap\n      value: 81.08262141256193\n    - type: cos_sim_f1\n      value: 73.82341501361338\n    - type: cos_sim_precision\n      value: 72.5720112159062\n    - type: cos_sim_recall\n      value: 75.11873350923483\n    - type: dot_accuracy\n      value: 86.66030875603504\n    - type: dot_ap\n      value: 76.6052349228621\n    - type: dot_f1\n      value: 70.13897280966768\n    - type: dot_precision\n      value: 64.70457079152732\n    - type: dot_recall\n      value: 76.56992084432717\n    - type: euclidean_accuracy\n      value: 88.37098408535495\n    - type: euclidean_ap\n      value: 81.12515230092113\n    - type: euclidean_f1\n      value: 74.10338225909379\n    - type: euclidean_precision\n      value: 71.76761433868974\n    - type: euclidean_recall\n      value: 76.59630606860158\n    - type: manhattan_accuracy\n      value: 88.34118137926924\n    - type: manhattan_ap\n      value: 80.95751834536561\n    - type: manhattan_f1\n      value: 73.9119496855346\n    - type: manhattan_precision\n      value: 70.625\n    - type: manhattan_recall\n      value: 77.5197889182058\n    - type: max_accuracy\n      value: 88.37098408535495\n    - type: max_ap\n      value: 81.12515230092113\n    - type: max_f1\n      value: 74.10338225909379\n  - task:\n      type: PairClassification\n    dataset:\n      type: mteb/twitterurlcorpus-pairclassification\n      name: MTEB TwitterURLCorpus\n      config: default\n      split: test\n      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n    metrics:\n    - type: cos_sim_accuracy\n      value: 89.79896767182831\n    - type: cos_sim_ap\n      value: 87.40071784061065\n    - type: cos_sim_f1\n      value: 79.87753144712087\n    - type: cos_sim_precision\n      value: 76.67304015296367\n    - type: cos_sim_recall\n      value: 83.3615645210964\n    - type: dot_accuracy\n      value: 88.95486474948578\n    - type: dot_ap\n      value: 86.00227979119943\n    - type: dot_f1\n      value: 78.54601474525914\n    - type: dot_precision\n      value: 75.00525394045535\n    - type: dot_recall\n      value: 82.43763473975977\n    - type: euclidean_accuracy\n      value: 89.7892653393876\n    - type: euclidean_ap\n      value: 87.42174706480819\n    - type: euclidean_f1\n      value: 80.07283321194465\n    - type: euclidean_precision\n      value: 75.96738529574351\n    - type: euclidean_recall\n      value: 84.6473668001232\n    - type: manhattan_accuracy\n      value: 89.8474793340319\n    - type: manhattan_ap\n      value: 87.47814292587448\n    - type: manhattan_f1\n      value: 80.15461150280949\n    - type: manhattan_precision\n      value: 74.88798234468\n    - type: manhattan_recall\n      value: 86.21804742839544\n    - type: max_accuracy\n      value: 89.8474793340319\n    - type: max_ap\n      value: 87.47814292587448\n    - type: max_f1\n      value: 80.15461150280949\n---\n\n# Model Summary\n\n> GritLM is a generative representational instruction tuned language model. It unifies text representation (embedding) and text generation into a single model achieving state-of-the-art performance on both types of tasks.\n\n- **Repository:** [ContextualAI/gritlm](https://github.com/ContextualAI/gritlm)\n- **Paper:** https://arxiv.org/abs/2402.09906\n- **Logs:** https://wandb.ai/muennighoff/gritlm/runs/0uui712t/overview\n- **Script:** https://github.com/ContextualAI/gritlm/blob/main/scripts/training/train_gritlm_7b.sh\n\n| Model | Description |\n|-------|-------------|\n| [GritLM 7B](https://hf.co/GritLM/GritLM-7B) | Mistral 7B finetuned using GRIT |\n| [GritLM 8x7B](https://hf.co/GritLM/GritLM-8x7B) | Mixtral 8x7B finetuned using GRIT |\n\n# Use\n\nThe model usage is documented [here](https://github.com/ContextualAI/gritlm?tab=readme-ov-file#inference).\n\n# Citation\n\n```bibtex\n@misc{muennighoff2024generative,\n      title={Generative Representational Instruction Tuning}, \n      author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela},\n      year={2024},\n      eprint={2402.09906},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n```\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "arxiv:2402.09906",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
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  "created_at": "2024-05-03T17:18:16.000Z",
  "pipeline_tag": "",
  "library_name": ""
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