ggml-org/bge-small-en-v1.5-q8_0-gguf - Free GGUF Download is indexed on GraySoft with repository links, GGUF quant files, and Hugging Face metadata. This page helps you pick a local model for guIDE or other runtimes. See related models in the same shard below.
Model Intelligence Sheet
ggml-org/bge-small-en-v1.5-q8_0-gguf overview
This model was converted to GGUF format from BAAI/bge-small-en-v1.5 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.
Downloads
360
Likes
6
Pipeline
feature-extraction
Library
sentence-transformers
Visibility
Public
Access
Open
Repository Files & Downloads
1 files detected
Direct downloads for all repository files
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| bge-small-en-v1.5-q8_0.gguf | GGUF | — | 34.99 MB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
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"frontmatter": {
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]
},
"hero_image_url": "",
"summary": "This model was converted to GGUF format from BAAI/bge-small-en-v1.5 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.",
"quick_links": [],
"benchmark_table_html": "",
"readme_markdown": "---\ntags:\n- sentence-transformers\n- feature-extraction\n- sentence-similarity\n- transformers\n- mteb\n- llama-cpp\n- gguf-my-repo\nlicense: mit\nlanguage:\n- en\nbase_model: BAAI/bge-small-en-v1.5\nmodel-index:\n- name: bge-small-en-v1.5\n results:\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonCounterfactualClassification (en)\n type: mteb/amazon_counterfactual\n config: en\n split: test\n revision: e8379541af4e31359cca9fbcf4b00f2671dba205\n metrics:\n - type: accuracy\n value: 73.79104477611939\n - type: ap\n value: 37.21923821573361\n - type: f1\n value: 68.0914945617093\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonPolarityClassification\n type: mteb/amazon_polarity\n config: default\n split: test\n revision: e2d317d38cd51312af73b3d32a06d1a08b442046\n metrics:\n - type: accuracy\n value: 92.75377499999999\n - type: ap\n value: 89.46766124546022\n - type: f1\n value: 92.73884001331487\n - task:\n type: Classification\n dataset:\n name: MTEB AmazonReviewsClassification (en)\n type: mteb/amazon_reviews_multi\n config: en\n split: test\n revision: 1399c76144fd37290681b995c656ef9b2e06e26d\n metrics:\n - type: accuracy\n value: 46.986\n - type: f1\n value: 46.55936786727896\n - task:\n type: Retrieval\n dataset:\n name: MTEB ArguAna\n type: arguana\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 35.846000000000004\n - type: map_at_10\n value: 51.388\n - type: map_at_100\n value: 52.132999999999996\n - type: map_at_1000\n value: 52.141000000000005\n - type: map_at_3\n value: 47.037\n - type: map_at_5\n value: 49.579\n - type: mrr_at_1\n value: 36.558\n - type: mrr_at_10\n value: 51.658\n - type: mrr_at_100\n value: 52.402\n - type: mrr_at_1000\n value: 52.410000000000004\n - type: mrr_at_3\n value: 47.345\n - type: mrr_at_5\n value: 49.797999999999995\n - type: ndcg_at_1\n value: 35.846000000000004\n - type: ndcg_at_10\n value: 59.550000000000004\n - type: ndcg_at_100\n value: 62.596\n - type: ndcg_at_1000\n value: 62.759\n - type: ndcg_at_3\n value: 50.666999999999994\n - type: ndcg_at_5\n value: 55.228\n - type: precision_at_1\n value: 35.846000000000004\n - type: precision_at_10\n value: 8.542\n - type: precision_at_100\n value: 0.984\n - type: precision_at_1000\n value: 0.1\n - type: precision_at_3\n value: 20.389\n - type: precision_at_5\n value: 14.438\n - type: recall_at_1\n value: 35.846000000000004\n - type: recall_at_10\n value: 85.42\n - type: recall_at_100\n value: 98.43499999999999\n - type: recall_at_1000\n value: 99.644\n - type: recall_at_3\n value: 61.166\n - type: recall_at_5\n value: 72.191\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringP2P\n type: mteb/arxiv-clustering-p2p\n config: default\n split: test\n revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d\n metrics:\n - type: v_measure\n value: 47.402770198163594\n - task:\n type: Clustering\n dataset:\n name: MTEB ArxivClusteringS2S\n type: mteb/arxiv-clustering-s2s\n config: default\n split: test\n revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53\n metrics:\n - type: v_measure\n value: 40.01545436974177\n - task:\n type: Reranking\n dataset:\n name: MTEB AskUbuntuDupQuestions\n type: mteb/askubuntudupquestions-reranking\n config: default\n split: test\n revision: 2000358ca161889fa9c082cb41daa8dcfb161a54\n metrics:\n - type: map\n value: 62.586465273207196\n - type: mrr\n value: 74.42169019038825\n - task:\n type: STS\n dataset:\n name: MTEB BIOSSES\n type: mteb/biosses-sts\n config: default\n split: test\n revision: d3fb88f8f02e40887cd149695127462bbcf29b4a\n metrics:\n - type: cos_sim_pearson\n value: 85.1891186537969\n - type: cos_sim_spearman\n value: 83.75492046087288\n - type: euclidean_pearson\n value: 84.11766204805357\n - type: euclidean_spearman\n value: 84.01456493126516\n - type: manhattan_pearson\n value: 84.2132950502772\n - type: manhattan_spearman\n value: 83.89227298813377\n - task:\n type: Classification\n dataset:\n name: MTEB Banking77Classification\n type: mteb/banking77\n config: default\n split: test\n revision: 0fd18e25b25c072e09e0d92ab615fda904d66300\n metrics:\n - type: accuracy\n value: 85.74025974025975\n - type: f1\n value: 85.71493566466381\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringP2P\n type: mteb/biorxiv-clustering-p2p\n config: default\n split: test\n revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40\n metrics:\n - type: v_measure\n value: 38.467181385006434\n - task:\n type: Clustering\n dataset:\n name: MTEB BiorxivClusteringS2S\n type: mteb/biorxiv-clustering-s2s\n config: default\n split: test\n revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908\n metrics:\n - type: v_measure\n value: 34.719496037339056\n - task:\n type: Retrieval\n dataset:\n name: MTEB CQADupstackAndroidRetrieval\n type: BeIR/cqadupstack\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 29.587000000000003\n - type: map_at_10\n value: 41.114\n - type: map_at_100\n value: 42.532\n - type: map_at_1000\n value: 42.661\n - type: map_at_3\n value: 37.483\n - type: map_at_5\n value: 39.652\n - type: mrr_at_1\n value: 36.338\n - type: mrr_at_10\n value: 46.763\n - type: mrr_at_100\n value: 47.393\n - type: mrr_at_1000\n value: 47.445\n - type: mrr_at_3\n value: 43.538\n - type: mrr_at_5\n value: 45.556000000000004\n - type: ndcg_at_1\n value: 36.338\n - type: ndcg_at_10\n value: 47.658\n - type: ndcg_at_100\n value: 52.824000000000005\n - type: ndcg_at_1000\n value: 54.913999999999994\n - type: ndcg_at_3\n value: 41.989\n - type: ndcg_at_5\n value: 44.944\n - type: precision_at_1\n value: 36.338\n - type: precision_at_10\n value: 9.156\n - type: precision_at_100\n value: 1.4789999999999999\n - type: precision_at_1000\n value: 0.196\n - type: precision_at_3\n value: 20.076\n - type: precision_at_5\n value: 14.85\n - type: recall_at_1\n value: 29.587000000000003\n - type: recall_at_10\n value: 60.746\n - type: recall_at_100\n value: 82.157\n - type: recall_at_1000\n value: 95.645\n - type: recall_at_3\n value: 44.821\n - type: recall_at_5\n value: 52.819\n - type: map_at_1\n value: 30.239\n - type: map_at_10\n value: 39.989000000000004\n - type: map_at_100\n value: 41.196\n - type: map_at_1000\n value: 41.325\n - type: map_at_3\n value: 37.261\n - type: map_at_5\n value: 38.833\n - type: mrr_at_1\n value: 37.516\n - type: mrr_at_10\n value: 46.177\n - type: mrr_at_100\n value: 46.806\n - type: mrr_at_1000\n value: 46.849000000000004\n - type: mrr_at_3\n value: 44.002\n - type: mrr_at_5\n value: 45.34\n - type: ndcg_at_1\n value: 37.516\n - type: ndcg_at_10\n value: 45.586\n - type: ndcg_at_100\n value: 49.897000000000006\n - type: ndcg_at_1000\n value: 51.955\n - type: ndcg_at_3\n value: 41.684\n - type: ndcg_at_5\n value: 43.617\n - type: precision_at_1\n value: 37.516\n - type: precision_at_10\n value: 8.522\n - type: precision_at_100\n value: 1.374\n - type: precision_at_1000\n value: 0.184\n - type: precision_at_3\n value: 20.105999999999998\n - type: precision_at_5\n value: 14.152999999999999\n - type: recall_at_1\n value: 30.239\n - type: recall_at_10\n value: 55.03\n - type: recall_at_100\n value: 73.375\n - type: recall_at_1000\n value: 86.29599999999999\n - type: recall_at_3\n value: 43.269000000000005\n - type: recall_at_5\n value: 48.878\n - type: map_at_1\n value: 38.338\n - type: map_at_10\n value: 50.468999999999994\n - type: map_at_100\n value: 51.553000000000004\n - type: map_at_1000\n value: 51.608\n - type: map_at_3\n value: 47.107\n - type: map_at_5\n value: 49.101\n - type: mrr_at_1\n value: 44.201\n - type: mrr_at_10\n value: 54.057\n - type: mrr_at_100\n value: 54.764\n - type: mrr_at_1000\n value: 54.791000000000004\n - type: mrr_at_3\n value: 51.56699999999999\n - type: mrr_at_5\n value: 53.05\n - type: ndcg_at_1\n value: 44.201\n - type: ndcg_at_10\n value: 56.379000000000005\n - type: ndcg_at_100\n value: 60.645\n - type: ndcg_at_1000\n value: 61.73499999999999\n - type: ndcg_at_3\n value: 50.726000000000006\n - type: ndcg_at_5\n value: 53.58500000000001\n - type: precision_at_1\n value: 44.201\n - type: precision_at_10\n value: 9.141\n - type: precision_at_100\n value: 1.216\n - type: precision_at_1000\n value: 0.135\n - type: precision_at_3\n value: 22.654\n - type: precision_at_5\n value: 15.723999999999998\n - type: recall_at_1\n value: 38.338\n - type: recall_at_10\n value: 70.30499999999999\n - type: recall_at_100\n value: 88.77199999999999\n - type: recall_at_1000\n value: 96.49799999999999\n - type: recall_at_3\n value: 55.218\n - type: recall_at_5\n value: 62.104000000000006\n - type: map_at_1\n value: 25.682\n - type: map_at_10\n value: 33.498\n - type: map_at_100\n value: 34.461000000000006\n - type: map_at_1000\n value: 34.544000000000004\n - type: map_at_3\n value: 30.503999999999998\n - type: map_at_5\n value: 32.216\n - type: mrr_at_1\n value: 27.683999999999997\n - type: mrr_at_10\n value: 35.467999999999996\n - type: mrr_at_100\n value: 36.32\n - type: mrr_at_1000\n value: 36.386\n - type: mrr_at_3\n value: 32.618\n - type: mrr_at_5\n value: 34.262\n - type: ndcg_at_1\n value: 27.683999999999997\n - type: ndcg_at_10\n value: 38.378\n - type: ndcg_at_100\n value: 43.288\n - type: ndcg_at_1000\n value: 45.413\n - type: ndcg_at_3\n value: 32.586\n - type: ndcg_at_5\n value: 35.499\n - type: precision_at_1\n value: 27.683999999999997\n - type: precision_at_10\n value: 5.864\n - type: precision_at_100\n value: 0.882\n - type: precision_at_1000\n value: 0.11\n - type: precision_at_3\n value: 13.446\n - type: precision_at_5\n value: 9.718\n - type: recall_at_1\n value: 25.682\n - type: recall_at_10\n value: 51.712\n - type: recall_at_100\n value: 74.446\n - type: recall_at_1000\n value: 90.472\n - type: recall_at_3\n value: 36.236000000000004\n - type: recall_at_5\n value: 43.234\n - type: map_at_1\n value: 16.073999999999998\n - type: map_at_10\n value: 24.352999999999998\n - type: map_at_100\n value: 25.438\n - type: map_at_1000\n value: 25.545\n - type: map_at_3\n value: 21.614\n - type: map_at_5\n value: 23.104\n - type: mrr_at_1\n value: 19.776\n - type: mrr_at_10\n value: 28.837000000000003\n - type: mrr_at_100\n value: 29.755\n - type: mrr_at_1000\n value: 29.817\n - type: mrr_at_3\n value: 26.201999999999998\n - type: mrr_at_5\n value: 27.714\n - type: ndcg_at_1\n value: 19.776\n - type: ndcg_at_10\n value: 29.701\n - type: ndcg_at_100\n value: 35.307\n - type: ndcg_at_1000\n value: 37.942\n - type: ndcg_at_3\n value: 24.764\n - type: ndcg_at_5\n value: 27.025\n - type: precision_at_1\n value: 19.776\n - type: precision_at_10\n value: 5.659\n - type: precision_at_100\n value: 0.971\n - type: precision_at_1000\n value: 0.133\n - type: precision_at_3\n value: 12.065\n - type: precision_at_5\n value: 8.905000000000001\n - type: recall_at_1\n value: 16.073999999999998\n - type: recall_at_10\n value: 41.647\n - type: recall_at_100\n value: 66.884\n - type: recall_at_1000\n value: 85.91499999999999\n - type: recall_at_3\n value: 27.916\n - type: recall_at_5\n value: 33.729\n - type: map_at_1\n value: 28.444999999999997\n - type: map_at_10\n value: 38.218999999999994\n - type: map_at_100\n value: 39.595\n - type: map_at_1000\n value: 39.709\n - type: map_at_3\n value: 35.586\n - type: map_at_5\n value: 36.895\n - type: mrr_at_1\n value: 34.841\n - type: mrr_at_10\n value: 44.106\n - type: mrr_at_100\n value: 44.98\n - type: mrr_at_1000\n value: 45.03\n - type: mrr_at_3\n value: 41.979\n - type: mrr_at_5\n value: 43.047999999999995\n - type: ndcg_at_1\n value: 34.841\n - type: ndcg_at_10\n value: 43.922\n - type: ndcg_at_100\n value: 49.504999999999995\n - type: ndcg_at_1000\n value: 51.675000000000004\n - type: ndcg_at_3\n value: 39.858\n - type: ndcg_at_5\n value: 41.408\n - type: precision_at_1\n value: 34.841\n - type: precision_at_10\n value: 7.872999999999999\n - type: precision_at_100\n value: 1.2449999999999999\n - type: precision_at_1000\n value: 0.161\n - type: precision_at_3\n value: 18.993\n - type: precision_at_5\n value: 13.032\n - type: recall_at_1\n value: 28.444999999999997\n - type: recall_at_10\n value: 54.984\n - type: recall_at_100\n value: 78.342\n - type: recall_at_1000\n value: 92.77\n - type: recall_at_3\n value: 42.842999999999996\n - type: recall_at_5\n value: 47.247\n - type: map_at_1\n value: 23.072\n - type: map_at_10\n value: 32.354\n - type: map_at_100\n value: 33.800000000000004\n - type: map_at_1000\n value: 33.908\n - type: map_at_3\n value: 29.232000000000003\n - type: map_at_5\n value: 31.049\n - type: mrr_at_1\n value: 29.110000000000003\n - type: mrr_at_10\n value: 38.03\n - type: mrr_at_100\n value: 39.032\n - type: mrr_at_1000\n value: 39.086999999999996\n - type: mrr_at_3\n value: 35.407\n - type: mrr_at_5\n value: 36.76\n - type: ndcg_at_1\n value: 29.110000000000003\n - type: ndcg_at_10\n value: 38.231\n - type: ndcg_at_100\n value: 44.425\n - type: ndcg_at_1000\n value: 46.771\n - type: ndcg_at_3\n value: 33.095\n - type: ndcg_at_5\n value: 35.459\n - type: precision_at_1\n value: 29.110000000000003\n - type: precision_at_10\n value: 7.215000000000001\n - type: precision_at_100\n value: 1.2109999999999999\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 16.058\n - type: precision_at_5\n value: 11.644\n - type: recall_at_1\n value: 23.072\n - type: recall_at_10\n value: 50.285999999999994\n - type: recall_at_100\n value: 76.596\n - type: recall_at_1000\n value: 92.861\n - type: recall_at_3\n value: 35.702\n - type: recall_at_5\n value: 42.152\n - type: map_at_1\n value: 24.937916666666666\n - type: map_at_10\n value: 33.755250000000004\n - type: map_at_100\n value: 34.955999999999996\n - type: map_at_1000\n value: 35.070499999999996\n - type: map_at_3\n value: 30.98708333333333\n - type: map_at_5\n value: 32.51491666666666\n - type: mrr_at_1\n value: 29.48708333333333\n - type: mrr_at_10\n value: 37.92183333333334\n - type: mrr_at_100\n value: 38.76583333333333\n - type: mrr_at_1000\n value: 38.82466666666667\n - type: mrr_at_3\n value: 35.45125\n - type: mrr_at_5\n value: 36.827000000000005\n - type: ndcg_at_1\n value: 29.48708333333333\n - type: ndcg_at_10\n value: 39.05225\n - type: ndcg_at_100\n value: 44.25983333333334\n - type: ndcg_at_1000\n value: 46.568333333333335\n - type: ndcg_at_3\n value: 34.271583333333325\n - type: ndcg_at_5\n value: 36.483916666666666\n - type: precision_at_1\n value: 29.48708333333333\n - type: precision_at_10\n value: 6.865749999999999\n - type: precision_at_100\n value: 1.1195833333333332\n - type: precision_at_1000\n value: 0.15058333333333335\n - type: precision_at_3\n value: 15.742083333333333\n - type: precision_at_5\n value: 11.221916666666667\n - type: recall_at_1\n value: 24.937916666666666\n - type: recall_at_10\n value: 50.650416666666665\n - type: recall_at_100\n value: 73.55383333333334\n - type: recall_at_1000\n value: 89.61691666666667\n - type: recall_at_3\n value: 37.27808333333334\n - type: recall_at_5\n value: 42.99475\n - type: map_at_1\n value: 23.947\n - type: map_at_10\n value: 30.575000000000003\n - type: map_at_100\n value: 31.465\n - type: map_at_1000\n value: 31.558000000000003\n - type: map_at_3\n value: 28.814\n - type: map_at_5\n value: 29.738999999999997\n - type: mrr_at_1\n value: 26.994\n - type: mrr_at_10\n value: 33.415\n - type: mrr_at_100\n value: 34.18\n - type: mrr_at_1000\n value: 34.245\n - type: mrr_at_3\n value: 31.621\n - type: mrr_at_5\n value: 32.549\n - type: ndcg_at_1\n value: 26.994\n - type: ndcg_at_10\n value: 34.482\n - type: ndcg_at_100\n value: 38.915\n - type: ndcg_at_1000\n value: 41.355\n - type: ndcg_at_3\n value: 31.139\n - type: ndcg_at_5\n value: 32.589\n - type: precision_at_1\n value: 26.994\n - type: precision_at_10\n value: 5.322\n - type: precision_at_100\n value: 0.8160000000000001\n - type: precision_at_1000\n value: 0.11100000000000002\n - type: precision_at_3\n value: 13.344000000000001\n - type: precision_at_5\n value: 8.988\n - type: recall_at_1\n value: 23.947\n - type: recall_at_10\n value: 43.647999999999996\n - type: recall_at_100\n value: 63.851\n - type: recall_at_1000\n value: 82.0\n - type: recall_at_3\n value: 34.288000000000004\n - type: recall_at_5\n value: 38.117000000000004\n - type: map_at_1\n value: 16.197\n - type: map_at_10\n value: 22.968\n - type: map_at_100\n value: 24.095\n - type: map_at_1000\n value: 24.217\n - type: map_at_3\n value: 20.771\n - type: map_at_5\n value: 21.995\n - type: mrr_at_1\n value: 19.511\n - type: mrr_at_10\n value: 26.55\n - type: mrr_at_100\n value: 27.500999999999998\n - type: mrr_at_1000\n value: 27.578999999999997\n - type: mrr_at_3\n value: 24.421\n - type: mrr_at_5\n value: 25.604\n - type: ndcg_at_1\n value: 19.511\n - type: ndcg_at_10\n value: 27.386\n - type: ndcg_at_100\n value: 32.828\n - type: ndcg_at_1000\n value: 35.739\n - type: ndcg_at_3\n value: 23.405\n - type: ndcg_at_5\n value: 25.255\n - type: precision_at_1\n value: 19.511\n - type: precision_at_10\n value: 5.017\n - type: precision_at_100\n value: 0.91\n - type: precision_at_1000\n value: 0.133\n - type: precision_at_3\n value: 11.023\n - type: precision_at_5\n value: 8.025\n - type: recall_at_1\n value: 16.197\n - type: recall_at_10\n value: 37.09\n - type: recall_at_100\n value: 61.778\n - type: recall_at_1000\n value: 82.56599999999999\n - type: recall_at_3\n value: 26.034000000000002\n - type: recall_at_5\n value: 30.762\n - type: map_at_1\n value: 25.41\n - type: map_at_10\n value: 33.655\n - type: map_at_100\n value: 34.892\n - type: map_at_1000\n value: 34.995\n - type: map_at_3\n value: 30.94\n - type: map_at_5\n value: 32.303\n - type: mrr_at_1\n value: 29.477999999999998\n - type: mrr_at_10\n value: 37.443\n - type: mrr_at_100\n value: 38.383\n - type: mrr_at_1000\n value: 38.440000000000005\n - type: mrr_at_3\n value: 34.949999999999996\n - type: mrr_at_5\n value: 36.228\n - type: ndcg_at_1\n value: 29.477999999999998\n - type: ndcg_at_10\n value: 38.769\n - type: ndcg_at_100\n value: 44.245000000000005\n - type: ndcg_at_1000\n value: 46.593\n - type: ndcg_at_3\n value: 33.623\n - type: ndcg_at_5\n value: 35.766\n - type: precision_at_1\n value: 29.477999999999998\n - type: precision_at_10\n value: 6.455\n - type: precision_at_100\n value: 1.032\n - type: precision_at_1000\n value: 0.135\n - type: precision_at_3\n value: 14.893999999999998\n - type: precision_at_5\n value: 10.485\n - type: recall_at_1\n value: 25.41\n - type: recall_at_10\n value: 50.669\n - type: recall_at_100\n value: 74.084\n - type: recall_at_1000\n value: 90.435\n - type: recall_at_3\n value: 36.679\n - type: recall_at_5\n value: 41.94\n - type: map_at_1\n value: 23.339\n - type: map_at_10\n value: 31.852000000000004\n - type: map_at_100\n value: 33.411\n - type: map_at_1000\n value: 33.62\n - type: map_at_3\n value: 28.929\n - type: map_at_5\n value: 30.542\n - type: mrr_at_1\n value: 28.063\n - type: mrr_at_10\n value: 36.301\n - type: mrr_at_100\n value: 37.288\n - type: mrr_at_1000\n value: 37.349\n - type: mrr_at_3\n value: 33.663\n - type: mrr_at_5\n value: 35.165\n - type: ndcg_at_1\n value: 28.063\n - type: ndcg_at_10\n value: 37.462\n - type: ndcg_at_100\n value: 43.620999999999995\n - type: ndcg_at_1000\n value: 46.211\n - type: ndcg_at_3\n value: 32.68\n - type: ndcg_at_5\n value: 34.981\n - type: precision_at_1\n value: 28.063\n - type: precision_at_10\n value: 7.1739999999999995\n - type: precision_at_100\n value: 1.486\n - type: precision_at_1000\n value: 0.23500000000000001\n - type: precision_at_3\n value: 15.217\n - type: precision_at_5\n value: 11.265\n - type: recall_at_1\n value: 23.339\n - type: recall_at_10\n value: 48.376999999999995\n - type: recall_at_100\n value: 76.053\n - type: recall_at_1000\n value: 92.455\n - type: recall_at_3\n value: 34.735\n - type: recall_at_5\n value: 40.71\n - type: map_at_1\n value: 18.925\n - type: map_at_10\n value: 26.017000000000003\n - type: map_at_100\n value: 27.034000000000002\n - type: map_at_1000\n value: 27.156000000000002\n - type: map_at_3\n value: 23.604\n - type: map_at_5\n value: 24.75\n - type: mrr_at_1\n value: 20.333000000000002\n - type: mrr_at_10\n value: 27.915\n - type: mrr_at_100\n value: 28.788000000000004\n - type: mrr_at_1000\n value: 28.877999999999997\n - type: mrr_at_3\n value: 25.446999999999996\n - type: mrr_at_5\n value: 26.648\n - type: ndcg_at_1\n value: 20.333000000000002\n - type: ndcg_at_10\n value: 30.673000000000002\n - type: ndcg_at_100\n value: 35.618\n - type: ndcg_at_1000\n value: 38.517\n - type: ndcg_at_3\n value: 25.71\n - type: ndcg_at_5\n value: 27.679\n - type: precision_at_1\n value: 20.333000000000002\n - type: precision_at_10\n value: 4.9910000000000005\n - type: precision_at_100\n value: 0.8130000000000001\n - type: precision_at_1000\n value: 0.117\n - type: precision_at_3\n value: 11.029\n - type: precision_at_5\n value: 7.8740000000000006\n - type: recall_at_1\n value: 18.925\n - type: recall_at_10\n value: 43.311\n - type: recall_at_100\n value: 66.308\n - type: recall_at_1000\n value: 87.49\n - type: recall_at_3\n value: 29.596\n - type: recall_at_5\n value: 34.245\n - task:\n type: Retrieval\n dataset:\n name: MTEB ClimateFEVER\n type: climate-fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 13.714\n - type: map_at_10\n value: 23.194\n - type: map_at_100\n value: 24.976000000000003\n - type: map_at_1000\n value: 25.166\n - type: map_at_3\n value: 19.709\n - type: map_at_5\n value: 21.523999999999997\n - type: mrr_at_1\n value: 30.619000000000003\n - type: mrr_at_10\n value: 42.563\n - type: mrr_at_100\n value: 43.386\n - type: mrr_at_1000\n value: 43.423\n - type: mrr_at_3\n value: 39.555\n - type: mrr_at_5\n value: 41.268\n - type: ndcg_at_1\n value: 30.619000000000003\n - type: ndcg_at_10\n value: 31.836\n - type: ndcg_at_100\n value: 38.652\n - type: ndcg_at_1000\n value: 42.088\n - type: ndcg_at_3\n value: 26.733\n - type: ndcg_at_5\n value: 28.435\n - type: precision_at_1\n value: 30.619000000000003\n - type: precision_at_10\n value: 9.751999999999999\n - type: precision_at_100\n value: 1.71\n - type: precision_at_1000\n value: 0.23500000000000001\n - type: precision_at_3\n value: 19.935\n - type: precision_at_5\n value: 14.984\n - type: recall_at_1\n value: 13.714\n - type: recall_at_10\n value: 37.26\n - type: recall_at_100\n value: 60.546\n - type: recall_at_1000\n value: 79.899\n - type: recall_at_3\n value: 24.325\n - type: recall_at_5\n value: 29.725\n - task:\n type: Retrieval\n dataset:\n name: MTEB DBPedia\n type: dbpedia-entity\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 8.462\n - type: map_at_10\n value: 18.637\n - type: map_at_100\n value: 26.131999999999998\n - type: map_at_1000\n value: 27.607\n - type: map_at_3\n value: 13.333\n - type: map_at_5\n value: 15.654000000000002\n - type: mrr_at_1\n value: 66.25\n - type: mrr_at_10\n value: 74.32600000000001\n - type: mrr_at_100\n value: 74.60900000000001\n - type: mrr_at_1000\n value: 74.62\n - type: mrr_at_3\n value: 72.667\n - type: mrr_at_5\n value: 73.817\n - type: ndcg_at_1\n value: 53.87499999999999\n - type: ndcg_at_10\n value: 40.028999999999996\n - type: ndcg_at_100\n value: 44.199\n - type: ndcg_at_1000\n value: 51.629999999999995\n - type: ndcg_at_3\n value: 44.113\n - type: ndcg_at_5\n value: 41.731\n - type: precision_at_1\n value: 66.25\n - type: precision_at_10\n value: 31.900000000000002\n - type: precision_at_100\n value: 10.043000000000001\n - type: precision_at_1000\n value: 1.926\n - type: precision_at_3\n value: 47.417\n - type: precision_at_5\n value: 40.65\n - type: recall_at_1\n value: 8.462\n - type: recall_at_10\n value: 24.293\n - type: recall_at_100\n value: 50.146\n - type: recall_at_1000\n value: 74.034\n - type: recall_at_3\n value: 14.967\n - type: recall_at_5\n value: 18.682000000000002\n - task:\n type: Classification\n dataset:\n name: MTEB EmotionClassification\n type: mteb/emotion\n config: default\n split: test\n revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37\n metrics:\n - type: accuracy\n value: 47.84499999999999\n - type: f1\n value: 42.48106691979349\n - task:\n type: Retrieval\n dataset:\n name: MTEB FEVER\n type: fever\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 74.034\n - type: map_at_10\n value: 82.76\n - type: map_at_100\n value: 82.968\n - type: map_at_1000\n value: 82.98299999999999\n - type: map_at_3\n value: 81.768\n - type: map_at_5\n value: 82.418\n - type: mrr_at_1\n value: 80.048\n - type: mrr_at_10\n value: 87.64999999999999\n - type: mrr_at_100\n value: 87.712\n - type: mrr_at_1000\n value: 87.713\n - type: mrr_at_3\n value: 87.01100000000001\n - type: mrr_at_5\n value: 87.466\n - type: ndcg_at_1\n value: 80.048\n - type: ndcg_at_10\n value: 86.643\n - type: ndcg_at_100\n value: 87.361\n - type: ndcg_at_1000\n value: 87.606\n - type: ndcg_at_3\n value: 85.137\n - type: ndcg_at_5\n value: 86.016\n - type: precision_at_1\n value: 80.048\n - type: precision_at_10\n value: 10.372\n - type: precision_at_100\n value: 1.093\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: precision_at_3\n value: 32.638\n - type: precision_at_5\n value: 20.177\n - type: recall_at_1\n value: 74.034\n - type: recall_at_10\n value: 93.769\n - type: recall_at_100\n value: 96.569\n - type: recall_at_1000\n value: 98.039\n - type: recall_at_3\n value: 89.581\n - type: recall_at_5\n value: 91.906\n - task:\n type: Retrieval\n dataset:\n name: MTEB FiQA2018\n type: fiqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 20.5\n - type: map_at_10\n value: 32.857\n - type: map_at_100\n value: 34.589\n - type: map_at_1000\n value: 34.778\n - type: map_at_3\n value: 29.160999999999998\n - type: map_at_5\n value: 31.033\n - type: mrr_at_1\n value: 40.123\n - type: mrr_at_10\n value: 48.776\n - type: mrr_at_100\n value: 49.495\n - type: mrr_at_1000\n value: 49.539\n - type: mrr_at_3\n value: 46.605000000000004\n - type: mrr_at_5\n value: 47.654\n - type: ndcg_at_1\n value: 40.123\n - type: ndcg_at_10\n value: 40.343\n - type: ndcg_at_100\n value: 46.56\n - type: ndcg_at_1000\n value: 49.777\n - type: ndcg_at_3\n value: 37.322\n - type: ndcg_at_5\n value: 37.791000000000004\n - type: precision_at_1\n value: 40.123\n - type: precision_at_10\n value: 11.08\n - type: precision_at_100\n value: 1.752\n - type: precision_at_1000\n value: 0.232\n - type: precision_at_3\n value: 24.897\n - type: precision_at_5\n value: 17.809\n - type: recall_at_1\n value: 20.5\n - type: recall_at_10\n value: 46.388\n - type: recall_at_100\n value: 69.552\n - type: recall_at_1000\n value: 89.011\n - type: recall_at_3\n value: 33.617999999999995\n - type: recall_at_5\n value: 38.211\n - task:\n type: Retrieval\n dataset:\n name: MTEB HotpotQA\n type: hotpotqa\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 39.135999999999996\n - type: map_at_10\n value: 61.673\n - type: map_at_100\n value: 62.562\n - type: map_at_1000\n value: 62.62\n - type: map_at_3\n value: 58.467999999999996\n - type: map_at_5\n value: 60.463\n - type: mrr_at_1\n value: 78.271\n - type: mrr_at_10\n value: 84.119\n - type: mrr_at_100\n value: 84.29299999999999\n - type: mrr_at_1000\n value: 84.299\n - type: mrr_at_3\n value: 83.18900000000001\n - type: mrr_at_5\n value: 83.786\n - type: ndcg_at_1\n value: 78.271\n - type: ndcg_at_10\n value: 69.935\n - type: ndcg_at_100\n value: 73.01299999999999\n - type: ndcg_at_1000\n value: 74.126\n - type: ndcg_at_3\n value: 65.388\n - type: ndcg_at_5\n value: 67.906\n - type: precision_at_1\n value: 78.271\n - type: precision_at_10\n value: 14.562\n - type: precision_at_100\n value: 1.6969999999999998\n - type: precision_at_1000\n value: 0.184\n - type: precision_at_3\n value: 41.841\n - type: precision_at_5\n value: 27.087\n - type: recall_at_1\n value: 39.135999999999996\n - type: recall_at_10\n value: 72.809\n - type: recall_at_100\n value: 84.86200000000001\n - type: recall_at_1000\n value: 92.208\n - type: recall_at_3\n value: 62.76199999999999\n - type: recall_at_5\n value: 67.718\n - task:\n type: Classification\n dataset:\n name: MTEB ImdbClassification\n type: mteb/imdb\n config: default\n split: test\n revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7\n metrics:\n - type: accuracy\n value: 90.60600000000001\n - type: ap\n value: 86.6579587804335\n - type: f1\n value: 90.5938853929307\n - task:\n type: Retrieval\n dataset:\n name: MTEB MSMARCO\n type: msmarco\n config: default\n split: dev\n revision: None\n metrics:\n - type: map_at_1\n value: 21.852\n - type: map_at_10\n value: 33.982\n - type: map_at_100\n value: 35.116\n - type: map_at_1000\n value: 35.167\n - type: map_at_3\n value: 30.134\n - type: map_at_5\n value: 32.340999999999994\n - type: mrr_at_1\n value: 22.479\n - type: mrr_at_10\n value: 34.594\n - type: mrr_at_100\n value: 35.672\n - type: mrr_at_1000\n value: 35.716\n - type: mrr_at_3\n value: 30.84\n - type: mrr_at_5\n value: 32.998\n - type: ndcg_at_1\n value: 22.493\n - type: ndcg_at_10\n value: 40.833000000000006\n - type: ndcg_at_100\n value: 46.357\n - type: ndcg_at_1000\n value: 47.637\n - type: ndcg_at_3\n value: 32.995999999999995\n - type: ndcg_at_5\n value: 36.919000000000004\n - type: precision_at_1\n value: 22.493\n - type: precision_at_10\n value: 6.465999999999999\n - type: precision_at_100\n value: 0.9249999999999999\n - type: precision_at_1000\n value: 0.104\n - type: precision_at_3\n value: 14.030999999999999\n - type: precision_at_5\n value: 10.413\n - type: recall_at_1\n value: 21.852\n - type: recall_at_10\n value: 61.934999999999995\n - type: recall_at_100\n value: 87.611\n - type: recall_at_1000\n value: 97.441\n - type: recall_at_3\n value: 40.583999999999996\n - type: recall_at_5\n value: 49.992999999999995\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPDomainClassification (en)\n type: mteb/mtop_domain\n config: en\n split: test\n revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf\n metrics:\n - type: accuracy\n value: 93.36069311445507\n - type: f1\n value: 93.16456330371453\n - task:\n type: Classification\n dataset:\n name: MTEB MTOPIntentClassification (en)\n type: mteb/mtop_intent\n config: en\n split: test\n revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba\n metrics:\n - type: accuracy\n value: 74.74692202462381\n - type: f1\n value: 58.17903579421599\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveIntentClassification (en)\n type: mteb/amazon_massive_intent\n config: en\n split: test\n revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7\n metrics:\n - type: accuracy\n value: 74.80833893745796\n - type: f1\n value: 72.70786592684664\n - task:\n type: Classification\n dataset:\n name: MTEB MassiveScenarioClassification (en)\n type: mteb/amazon_massive_scenario\n config: en\n split: test\n revision: 7d571f92784cd94a019292a1f45445077d0ef634\n metrics:\n - type: accuracy\n value: 78.69872225958305\n - type: f1\n value: 78.61626934504731\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringP2P\n type: mteb/medrxiv-clustering-p2p\n config: default\n split: test\n revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73\n metrics:\n - type: v_measure\n value: 33.058658628717694\n - task:\n type: Clustering\n dataset:\n name: MTEB MedrxivClusteringS2S\n type: mteb/medrxiv-clustering-s2s\n config: default\n split: test\n revision: 35191c8c0dca72d8ff3efcd72aa802307d469663\n metrics:\n - type: v_measure\n value: 30.85561739360599\n - task:\n type: Reranking\n dataset:\n name: MTEB MindSmallReranking\n type: mteb/mind_small\n config: default\n split: test\n revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69\n metrics:\n - type: map\n value: 31.290259910144385\n - type: mrr\n value: 32.44223046102856\n - task:\n type: Retrieval\n dataset:\n name: MTEB NFCorpus\n type: nfcorpus\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 5.288\n - type: map_at_10\n value: 12.267999999999999\n - type: map_at_100\n value: 15.557000000000002\n - type: map_at_1000\n value: 16.98\n - type: map_at_3\n value: 8.866\n - type: map_at_5\n value: 10.418\n - type: mrr_at_1\n value: 43.653\n - type: mrr_at_10\n value: 52.681\n - type: mrr_at_100\n value: 53.315999999999995\n - type: mrr_at_1000\n value: 53.357\n - type: mrr_at_3\n value: 51.393\n - type: mrr_at_5\n value: 51.903999999999996\n - type: ndcg_at_1\n value: 42.415000000000006\n - type: ndcg_at_10\n value: 34.305\n - type: ndcg_at_100\n value: 30.825999999999997\n - type: ndcg_at_1000\n value: 39.393\n - type: ndcg_at_3\n value: 39.931\n - type: ndcg_at_5\n value: 37.519999999999996\n - type: precision_at_1\n value: 43.653\n - type: precision_at_10\n value: 25.728\n - type: precision_at_100\n value: 7.932\n - type: precision_at_1000\n value: 2.07\n - type: precision_at_3\n value: 38.184000000000005\n - type: precision_at_5\n value: 32.879000000000005\n - type: recall_at_1\n value: 5.288\n - type: recall_at_10\n value: 16.195\n - type: recall_at_100\n value: 31.135\n - type: recall_at_1000\n value: 61.531000000000006\n - type: recall_at_3\n value: 10.313\n - type: recall_at_5\n value: 12.754999999999999\n - task:\n type: Retrieval\n dataset:\n name: MTEB NQ\n type: nq\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 28.216\n - type: map_at_10\n value: 42.588\n - type: map_at_100\n value: 43.702999999999996\n - type: map_at_1000\n value: 43.739\n - type: map_at_3\n value: 38.177\n - type: map_at_5\n value: 40.754000000000005\n - type: mrr_at_1\n value: 31.866\n - type: mrr_at_10\n value: 45.189\n - type: mrr_at_100\n value: 46.056000000000004\n - type: mrr_at_1000\n value: 46.081\n - type: mrr_at_3\n value: 41.526999999999994\n - type: mrr_at_5\n value: 43.704\n - type: ndcg_at_1\n value: 31.837\n - type: ndcg_at_10\n value: 50.178\n - type: ndcg_at_100\n value: 54.98800000000001\n - type: ndcg_at_1000\n value: 55.812\n - type: ndcg_at_3\n value: 41.853\n - type: ndcg_at_5\n value: 46.153\n - type: precision_at_1\n value: 31.837\n - type: precision_at_10\n value: 8.43\n - type: precision_at_100\n value: 1.1119999999999999\n - type: precision_at_1000\n value: 0.11900000000000001\n - type: precision_at_3\n value: 19.023\n - type: precision_at_5\n value: 13.911000000000001\n - type: recall_at_1\n value: 28.216\n - type: recall_at_10\n value: 70.8\n - type: recall_at_100\n value: 91.857\n - type: recall_at_1000\n value: 97.941\n - type: recall_at_3\n value: 49.196\n - type: recall_at_5\n value: 59.072\n - task:\n type: Retrieval\n dataset:\n name: MTEB QuoraRetrieval\n type: quora\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 71.22800000000001\n - type: map_at_10\n value: 85.115\n - type: map_at_100\n value: 85.72\n - type: map_at_1000\n value: 85.737\n - type: map_at_3\n value: 82.149\n - type: map_at_5\n value: 84.029\n - type: mrr_at_1\n value: 81.96\n - type: mrr_at_10\n value: 88.00200000000001\n - type: mrr_at_100\n value: 88.088\n - type: mrr_at_1000\n value: 88.089\n - type: mrr_at_3\n value: 87.055\n - type: mrr_at_5\n value: 87.715\n - type: ndcg_at_1\n value: 82.01\n - type: ndcg_at_10\n value: 88.78\n - type: ndcg_at_100\n value: 89.91\n - type: ndcg_at_1000\n value: 90.013\n - type: ndcg_at_3\n value: 85.957\n - type: ndcg_at_5\n value: 87.56\n - type: precision_at_1\n value: 82.01\n - type: precision_at_10\n value: 13.462\n - type: precision_at_100\n value: 1.528\n - type: precision_at_1000\n value: 0.157\n - type: precision_at_3\n value: 37.553\n - type: precision_at_5\n value: 24.732000000000003\n - type: recall_at_1\n value: 71.22800000000001\n - type: recall_at_10\n value: 95.69\n - type: recall_at_100\n value: 99.531\n - type: recall_at_1000\n value: 99.98\n - type: recall_at_3\n value: 87.632\n - type: recall_at_5\n value: 92.117\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClustering\n type: mteb/reddit-clustering\n config: default\n split: test\n revision: 24640382cdbf8abc73003fb0fa6d111a705499eb\n metrics:\n - type: v_measure\n value: 52.31768034366916\n - task:\n type: Clustering\n dataset:\n name: MTEB RedditClusteringP2P\n type: mteb/reddit-clustering-p2p\n config: default\n split: test\n revision: 282350215ef01743dc01b456c7f5241fa8937f16\n metrics:\n - type: v_measure\n value: 60.640266772723606\n - task:\n type: Retrieval\n dataset:\n name: MTEB SCIDOCS\n type: scidocs\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 4.7780000000000005\n - type: map_at_10\n value: 12.299\n - type: map_at_100\n value: 14.363000000000001\n - type: map_at_1000\n value: 14.71\n - type: map_at_3\n value: 8.738999999999999\n - type: map_at_5\n value: 10.397\n - type: mrr_at_1\n value: 23.599999999999998\n - type: mrr_at_10\n value: 34.845\n - type: mrr_at_100\n value: 35.916\n - type: mrr_at_1000\n value: 35.973\n - type: mrr_at_3\n value: 31.7\n - type: mrr_at_5\n value: 33.535\n - type: ndcg_at_1\n value: 23.599999999999998\n - type: ndcg_at_10\n value: 20.522000000000002\n - type: ndcg_at_100\n value: 28.737000000000002\n - type: ndcg_at_1000\n value: 34.596\n - type: ndcg_at_3\n value: 19.542\n - type: ndcg_at_5\n value: 16.958000000000002\n - type: precision_at_1\n value: 23.599999999999998\n - type: precision_at_10\n value: 10.67\n - type: precision_at_100\n value: 2.259\n - type: precision_at_1000\n value: 0.367\n - type: precision_at_3\n value: 18.333\n - type: precision_at_5\n value: 14.879999999999999\n - type: recall_at_1\n value: 4.7780000000000005\n - type: recall_at_10\n value: 21.617\n - type: recall_at_100\n value: 45.905\n - type: recall_at_1000\n value: 74.42\n - type: recall_at_3\n value: 11.148\n - type: recall_at_5\n value: 15.082999999999998\n - task:\n type: STS\n dataset:\n name: MTEB SICK-R\n type: mteb/sickr-sts\n config: default\n split: test\n revision: a6ea5a8cab320b040a23452cc28066d9beae2cee\n metrics:\n - type: cos_sim_pearson\n value: 83.22372750297885\n - type: cos_sim_spearman\n value: 79.40972617119405\n - type: euclidean_pearson\n value: 80.6101072020434\n - type: euclidean_spearman\n value: 79.53844217225202\n - type: manhattan_pearson\n value: 80.57265975286111\n - type: manhattan_spearman\n value: 79.46335611792958\n - task:\n type: STS\n dataset:\n name: MTEB STS12\n type: mteb/sts12-sts\n config: default\n split: test\n revision: a0d554a64d88156834ff5ae9920b964011b16384\n metrics:\n - type: cos_sim_pearson\n value: 85.43713315520749\n - type: cos_sim_spearman\n value: 77.44128693329532\n - type: euclidean_pearson\n value: 81.63869928101123\n - type: euclidean_spearman\n value: 77.29512977961515\n - type: manhattan_pearson\n value: 81.63704185566183\n - type: manhattan_spearman\n value: 77.29909412738657\n - task:\n type: STS\n dataset:\n name: MTEB STS13\n type: mteb/sts13-sts\n config: default\n split: test\n revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca\n metrics:\n - type: cos_sim_pearson\n value: 81.59451537860527\n - type: cos_sim_spearman\n value: 82.97994638856723\n - type: euclidean_pearson\n value: 82.89478688288412\n - type: euclidean_spearman\n value: 83.58740751053104\n - type: manhattan_pearson\n value: 82.69140840941608\n - type: manhattan_spearman\n value: 83.33665956040555\n - task:\n type: STS\n dataset:\n name: MTEB STS14\n type: mteb/sts14-sts\n config: default\n split: test\n revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375\n metrics:\n - type: cos_sim_pearson\n value: 82.00756527711764\n - type: cos_sim_spearman\n value: 81.83560996841379\n - type: euclidean_pearson\n value: 82.07684151976518\n - type: euclidean_spearman\n value: 82.00913052060511\n - type: manhattan_pearson\n value: 82.05690778488794\n - type: manhattan_spearman\n value: 82.02260252019525\n - task:\n type: STS\n dataset:\n name: MTEB STS15\n type: mteb/sts15-sts\n config: default\n split: test\n revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3\n metrics:\n - type: cos_sim_pearson\n value: 86.13710262895447\n - type: cos_sim_spearman\n value: 87.26412811156248\n - type: euclidean_pearson\n value: 86.94151453230228\n - type: euclidean_spearman\n value: 87.5363796699571\n - type: manhattan_pearson\n value: 86.86989424083748\n - type: manhattan_spearman\n value: 87.47315940781353\n - task:\n type: STS\n dataset:\n name: MTEB STS16\n type: mteb/sts16-sts\n config: default\n split: test\n revision: 4d8694f8f0e0100860b497b999b3dbed754a0513\n metrics:\n - type: cos_sim_pearson\n value: 83.0230597603627\n - type: cos_sim_spearman\n value: 84.93344499318864\n - type: euclidean_pearson\n value: 84.23754743431141\n - type: euclidean_spearman\n value: 85.09707376597099\n - type: manhattan_pearson\n value: 84.04325160987763\n - type: manhattan_spearman\n value: 84.89353071339909\n - task:\n type: STS\n dataset:\n name: MTEB STS17 (en-en)\n type: mteb/sts17-crosslingual-sts\n config: en-en\n split: test\n revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d\n metrics:\n - type: cos_sim_pearson\n value: 86.75620824563921\n - type: cos_sim_spearman\n value: 87.15065513706398\n - type: euclidean_pearson\n value: 88.26281533633521\n - type: euclidean_spearman\n value: 87.51963738643983\n - type: manhattan_pearson\n value: 88.25599267618065\n - type: manhattan_spearman\n value: 87.58048736047483\n - task:\n type: STS\n dataset:\n name: MTEB STS22 (en)\n type: mteb/sts22-crosslingual-sts\n config: en\n split: test\n revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80\n metrics:\n - type: cos_sim_pearson\n value: 64.74645319195137\n - type: cos_sim_spearman\n value: 65.29996325037214\n - type: euclidean_pearson\n value: 67.04297794086443\n - type: euclidean_spearman\n value: 65.43841726694343\n - type: manhattan_pearson\n value: 67.39459955690904\n - type: manhattan_spearman\n value: 65.92864704413651\n - task:\n type: STS\n dataset:\n name: MTEB STSBenchmark\n type: mteb/stsbenchmark-sts\n config: default\n split: test\n revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831\n metrics:\n - type: cos_sim_pearson\n value: 84.31291020270801\n - type: cos_sim_spearman\n value: 85.86473738688068\n - type: euclidean_pearson\n value: 85.65537275064152\n - type: euclidean_spearman\n value: 86.13087454209642\n - type: manhattan_pearson\n value: 85.43946955047609\n - type: manhattan_spearman\n value: 85.91568175344916\n - task:\n type: Reranking\n dataset:\n name: MTEB SciDocsRR\n type: mteb/scidocs-reranking\n config: default\n split: test\n revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab\n metrics:\n - type: map\n value: 85.93798118350695\n - type: mrr\n value: 95.93536274908824\n - task:\n type: Retrieval\n dataset:\n name: MTEB SciFact\n type: scifact\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 57.594\n - type: map_at_10\n value: 66.81899999999999\n - type: map_at_100\n value: 67.368\n - type: map_at_1000\n value: 67.4\n - type: map_at_3\n value: 64.061\n - type: map_at_5\n value: 65.47\n - type: mrr_at_1\n value: 60.667\n - type: mrr_at_10\n value: 68.219\n - type: mrr_at_100\n value: 68.655\n - type: mrr_at_1000\n value: 68.684\n - type: mrr_at_3\n value: 66.22200000000001\n - type: mrr_at_5\n value: 67.289\n - type: ndcg_at_1\n value: 60.667\n - type: ndcg_at_10\n value: 71.275\n - type: ndcg_at_100\n value: 73.642\n - type: ndcg_at_1000\n value: 74.373\n - type: ndcg_at_3\n value: 66.521\n - type: ndcg_at_5\n value: 68.581\n - type: precision_at_1\n value: 60.667\n - type: precision_at_10\n value: 9.433\n - type: precision_at_100\n value: 1.0699999999999998\n - type: precision_at_1000\n value: 0.11299999999999999\n - type: precision_at_3\n value: 25.556\n - type: precision_at_5\n value: 16.8\n - type: recall_at_1\n value: 57.594\n - type: recall_at_10\n value: 83.622\n - type: recall_at_100\n value: 94.167\n - type: recall_at_1000\n value: 99.667\n - type: recall_at_3\n value: 70.64399999999999\n - type: recall_at_5\n value: 75.983\n - task:\n type: PairClassification\n dataset:\n name: MTEB SprintDuplicateQuestions\n type: mteb/sprintduplicatequestions-pairclassification\n config: default\n split: test\n revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46\n metrics:\n - type: cos_sim_accuracy\n value: 99.85841584158416\n - type: cos_sim_ap\n value: 96.66996142314342\n - type: cos_sim_f1\n value: 92.83208020050125\n - type: cos_sim_precision\n value: 93.06532663316584\n - type: cos_sim_recall\n value: 92.60000000000001\n - type: dot_accuracy\n value: 99.85841584158416\n - type: dot_ap\n value: 96.6775307676576\n - type: dot_f1\n value: 92.69289729177312\n - type: dot_precision\n value: 94.77533960292581\n - type: dot_recall\n value: 90.7\n - type: euclidean_accuracy\n value: 99.86138613861387\n - type: euclidean_ap\n value: 96.6338454403108\n - type: euclidean_f1\n value: 92.92214357937311\n - type: euclidean_precision\n value: 93.96728016359918\n - type: euclidean_recall\n value: 91.9\n - type: manhattan_accuracy\n value: 99.86237623762376\n - type: manhattan_ap\n value: 96.60370449645053\n - type: manhattan_f1\n value: 92.91177970423253\n - type: manhattan_precision\n value: 94.7970863683663\n - type: manhattan_recall\n value: 91.10000000000001\n - type: max_accuracy\n value: 99.86237623762376\n - type: max_ap\n value: 96.6775307676576\n - type: max_f1\n value: 92.92214357937311\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClustering\n type: mteb/stackexchange-clustering\n config: default\n split: test\n revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259\n metrics:\n - type: v_measure\n value: 60.77977058695198\n - task:\n type: Clustering\n dataset:\n name: MTEB StackExchangeClusteringP2P\n type: mteb/stackexchange-clustering-p2p\n config: default\n split: test\n revision: 815ca46b2622cec33ccafc3735d572c266efdb44\n metrics:\n - type: v_measure\n value: 35.2725272535638\n - task:\n type: Reranking\n dataset:\n name: MTEB StackOverflowDupQuestions\n type: mteb/stackoverflowdupquestions-reranking\n config: default\n split: test\n revision: e185fbe320c72810689fc5848eb6114e1ef5ec69\n metrics:\n - type: map\n value: 53.64052466362125\n - type: mrr\n value: 54.533067014684654\n - task:\n type: Summarization\n dataset:\n name: MTEB SummEval\n type: mteb/summeval\n config: default\n split: test\n revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c\n metrics:\n - type: cos_sim_pearson\n value: 30.677624219206578\n - type: cos_sim_spearman\n value: 30.121368518123447\n - type: dot_pearson\n value: 30.69870088041608\n - type: dot_spearman\n value: 29.61284927093751\n - task:\n type: Retrieval\n dataset:\n name: MTEB TRECCOVID\n type: trec-covid\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 0.22\n - type: map_at_10\n value: 1.855\n - type: map_at_100\n value: 9.885\n - type: map_at_1000\n value: 23.416999999999998\n - type: map_at_3\n value: 0.637\n - type: map_at_5\n value: 1.024\n - type: mrr_at_1\n value: 88.0\n - type: mrr_at_10\n value: 93.067\n - type: mrr_at_100\n value: 93.067\n - type: mrr_at_1000\n value: 93.067\n - type: mrr_at_3\n value: 92.667\n - type: mrr_at_5\n value: 93.067\n - type: ndcg_at_1\n value: 82.0\n - type: ndcg_at_10\n value: 75.899\n - type: ndcg_at_100\n value: 55.115\n - type: ndcg_at_1000\n value: 48.368\n - type: ndcg_at_3\n value: 79.704\n - type: ndcg_at_5\n value: 78.39699999999999\n - type: precision_at_1\n value: 88.0\n - type: precision_at_10\n value: 79.60000000000001\n - type: precision_at_100\n value: 56.06\n - type: precision_at_1000\n value: 21.206\n - type: precision_at_3\n value: 84.667\n - type: precision_at_5\n value: 83.2\n - type: recall_at_1\n value: 0.22\n - type: recall_at_10\n value: 2.078\n - type: recall_at_100\n value: 13.297\n - type: recall_at_1000\n value: 44.979\n - type: recall_at_3\n value: 0.6689999999999999\n - type: recall_at_5\n value: 1.106\n - task:\n type: Retrieval\n dataset:\n name: MTEB Touche2020\n type: webis-touche2020\n config: default\n split: test\n revision: None\n metrics:\n - type: map_at_1\n value: 2.258\n - type: map_at_10\n value: 10.439\n - type: map_at_100\n value: 16.89\n - type: map_at_1000\n value: 18.407999999999998\n - type: map_at_3\n value: 5.668\n - type: map_at_5\n value: 7.718\n - type: mrr_at_1\n value: 32.653\n - type: mrr_at_10\n value: 51.159\n - type: mrr_at_100\n value: 51.714000000000006\n - type: mrr_at_1000\n value: 51.714000000000006\n - type: mrr_at_3\n value: 47.959\n - type: mrr_at_5\n value: 50.407999999999994\n - type: ndcg_at_1\n value: 29.592000000000002\n - type: ndcg_at_10\n value: 26.037\n - type: ndcg_at_100\n value: 37.924\n - type: ndcg_at_1000\n value: 49.126999999999995\n - type: ndcg_at_3\n value: 30.631999999999998\n - type: ndcg_at_5\n value: 28.571\n - type: precision_at_1\n value: 32.653\n - type: precision_at_10\n value: 22.857\n - type: precision_at_100\n value: 7.754999999999999\n - type: precision_at_1000\n value: 1.529\n - type: precision_at_3\n value: 34.014\n - type: precision_at_5\n value: 29.796\n - type: recall_at_1\n value: 2.258\n - type: recall_at_10\n value: 16.554\n - type: recall_at_100\n value: 48.439\n - type: recall_at_1000\n value: 82.80499999999999\n - type: recall_at_3\n value: 7.283\n - type: recall_at_5\n value: 10.732\n - task:\n type: Classification\n dataset:\n name: MTEB ToxicConversationsClassification\n type: mteb/toxic_conversations_50k\n config: default\n split: test\n revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c\n metrics:\n - type: accuracy\n value: 69.8858\n - type: ap\n value: 13.835684144362109\n - type: f1\n value: 53.803351693244586\n - task:\n type: Classification\n dataset:\n name: MTEB TweetSentimentExtractionClassification\n type: mteb/tweet_sentiment_extraction\n config: default\n split: test\n revision: d604517c81ca91fe16a244d1248fc021f9ecee7a\n metrics:\n - type: accuracy\n value: 60.50650820599886\n - type: f1\n value: 60.84357825979259\n - task:\n type: Clustering\n dataset:\n name: MTEB TwentyNewsgroupsClustering\n type: mteb/twentynewsgroups-clustering\n config: default\n split: test\n revision: 6125ec4e24fa026cec8a478383ee943acfbd5449\n metrics:\n - type: v_measure\n value: 48.52131044852134\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterSemEval2015\n type: mteb/twittersemeval2015-pairclassification\n config: default\n split: test\n revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1\n metrics:\n - type: cos_sim_accuracy\n value: 85.59337187816654\n - type: cos_sim_ap\n value: 73.23925826533437\n - type: cos_sim_f1\n value: 67.34693877551021\n - type: cos_sim_precision\n value: 62.40432237730752\n - type: cos_sim_recall\n value: 73.13984168865434\n - type: dot_accuracy\n value: 85.31322644096085\n - type: dot_ap\n value: 72.30723963807422\n - type: dot_f1\n value: 66.47051612112296\n - type: dot_precision\n value: 62.0792305930845\n - type: dot_recall\n value: 71.53034300791556\n - type: euclidean_accuracy\n value: 85.61125350181797\n - type: euclidean_ap\n value: 73.32843720487845\n - type: euclidean_f1\n value: 67.36549633745895\n - type: euclidean_precision\n value: 64.60755813953489\n - type: euclidean_recall\n value: 70.36939313984169\n - type: manhattan_accuracy\n value: 85.63509566668654\n - type: manhattan_ap\n value: 73.16658488311325\n - type: manhattan_f1\n value: 67.20597386434349\n - type: manhattan_precision\n value: 63.60424028268551\n - type: manhattan_recall\n value: 71.2401055408971\n - type: max_accuracy\n value: 85.63509566668654\n - type: max_ap\n value: 73.32843720487845\n - type: max_f1\n value: 67.36549633745895\n - task:\n type: PairClassification\n dataset:\n name: MTEB TwitterURLCorpus\n type: mteb/twitterurlcorpus-pairclassification\n config: default\n split: test\n revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf\n metrics:\n - type: cos_sim_accuracy\n value: 88.33779640625606\n - type: cos_sim_ap\n value: 84.83868375898157\n - type: cos_sim_f1\n value: 77.16506154017773\n - type: cos_sim_precision\n value: 74.62064005753327\n - type: cos_sim_recall\n value: 79.88912842623961\n - type: dot_accuracy\n value: 88.02732176815307\n - type: dot_ap\n value: 83.95089283763002\n - type: dot_f1\n value: 76.29635101196631\n - type: dot_precision\n value: 73.31771720613288\n - type: dot_recall\n value: 79.52725592854944\n - type: euclidean_accuracy\n value: 88.44452206310397\n - type: euclidean_ap\n value: 84.98384576824827\n - type: euclidean_f1\n value: 77.29311047696697\n - type: euclidean_precision\n value: 74.51232583065381\n - type: euclidean_recall\n value: 80.28949799815214\n - type: manhattan_accuracy\n value: 88.47362906042613\n - type: manhattan_ap\n value: 84.91421462218432\n - type: manhattan_f1\n value: 77.05107637204792\n - type: manhattan_precision\n value: 74.74484256243214\n - type: manhattan_recall\n value: 79.50415768401602\n - type: max_accuracy\n value: 88.47362906042613\n - type: max_ap\n value: 84.98384576824827\n - type: max_f1\n value: 77.29311047696697\n---\n\n# ggml-org/bge-small-en-v1.5-Q8_0-GGUF\nThis model was converted to GGUF format from [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.\nRefer to the [original model card](https://huggingface.co/BAAI/bge-small-en-v1.5) for more details on the model.\n\n## Use with llama.cpp\nInstall llama.cpp through brew (works on Mac and Linux)\n\n```bash\nbrew install llama.cpp\n\n```\nInvoke the llama.cpp server or the CLI.\n\n### CLI:\n```bash\nllama-cli --hf-repo ggml-org/bge-small-en-v1.5-Q8_0-GGUF --hf-file bge-small-en-v1.5-q8_0.gguf -p \"The meaning to life and the universe is\"\n```\n\n### Server:\n```bash\nllama-server --hf-repo ggml-org/bge-small-en-v1.5-Q8_0-GGUF --hf-file bge-small-en-v1.5-q8_0.gguf -c 2048\n```\n\nNote: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.\n\nStep 1: Clone llama.cpp from GitHub.\n```\ngit clone https://github.com/ggerganov/llama.cpp\n```\n\nStep 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).\n```\ncd llama.cpp && LLAMA_CURL=1 make\n```\n\nStep 3: Run inference through the main binary.\n```\n./llama-cli --hf-repo ggml-org/bge-small-en-v1.5-Q8_0-GGUF --hf-file bge-small-en-v1.5-q8_0.gguf -p \"The meaning to life and the universe is\"\n```\nor \n```\n./llama-server --hf-repo ggml-org/bge-small-en-v1.5-Q8_0-GGUF --hf-file bge-small-en-v1.5-q8_0.gguf -c 2048\n```\n",
"related_quantizations": []
},
"tags": [
"sentence-transformers",
"gguf",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:quantized:BAAI/bge-small-en-v1.5",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
],
"likes": 6,
"downloads": 360,
"gated": false,
"private": false,
"last_modified": "2025-02-06T09:40:16.000Z",
"created_at": "2025-02-06T09:26:01.000Z",
"pipeline_tag": "feature-extraction",
"library_name": "sentence-transformers"
}
Source payload excerpt (from Hugging Face API)
{
"_id": "67a48029401f5d8b4f96e290",
"id": "ggml-org/bge-small-en-v1.5-Q8_0-GGUF",
"modelId": "ggml-org/bge-small-en-v1.5-Q8_0-GGUF",
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