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0.004900076426565647, -0.05747111514210701, -0.04799460619688034, -0.04504697024822235, -0.022428296506404877, -0.04022650048136711, 0.03489668667316437, 0.013329615816473961, -0.0015142650809139013, 0.01967037096619606, 0.031717341393232346, -0.06311529874801636, 0.034045957028865814, 0.00040763692231848836, 0.0408024825155735] \ No newline at end of file diff --git a/config.py b/config.py new file mode 100644 index 0000000000000000000000000000000000000000..20fab04e6aa50caa2f02403bbccfecbde5943fcf --- /dev/null +++ b/config.py @@ -0,0 +1,20 @@ +import os +from dotenv import load_dotenv + +def set_environment(): + # Load .env only if running locally + if not os.getenv("HF_SPACE_ID"): + load_dotenv() + + required_keys = [ + "OPENAI_API_KEY", + "OPENROUTER_API_KEY", + "GEMINI_API_KEY", + "GROQ_API_KEY" + ] + missing_keys = [key for key in required_keys if not os.getenv(key)] + + if missing_keys: + print(f"Warning: Missing environment variables: {missing_keys}") + else: + print("All required API keys loaded successfully!") \ No newline at end of file diff --git a/embeddings_cache/models--intfloat--e5-base/.no_exist/b533fe4636f4a2507c08ddab40644d20b0006d6a/adapter_config.json b/embeddings_cache/models--intfloat--e5-base/.no_exist/b533fe4636f4a2507c08ddab40644d20b0006d6a/adapter_config.json new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/embeddings_cache/models--intfloat--e5-base/.no_exist/b533fe4636f4a2507c08ddab40644d20b0006d6a/added_tokens.json b/embeddings_cache/models--intfloat--e5-base/.no_exist/b533fe4636f4a2507c08ddab40644d20b0006d6a/added_tokens.json new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/embeddings_cache/models--intfloat--e5-base/.no_exist/b533fe4636f4a2507c08ddab40644d20b0006d6a/chat_template.jinja b/embeddings_cache/models--intfloat--e5-base/.no_exist/b533fe4636f4a2507c08ddab40644d20b0006d6a/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/embeddings_cache/models--intfloat--e5-base/.no_exist/b533fe4636f4a2507c08ddab40644d20b0006d6a/config_sentence_transformers.json b/embeddings_cache/models--intfloat--e5-base/.no_exist/b533fe4636f4a2507c08ddab40644d20b0006d6a/config_sentence_transformers.json new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/embeddings_cache/models--intfloat--e5-base/refs/main b/embeddings_cache/models--intfloat--e5-base/refs/main new file mode 100644 index 0000000000000000000000000000000000000000..52539ff98d6a8169f6ee6237a979ed263e9d01bb --- /dev/null +++ b/embeddings_cache/models--intfloat--e5-base/refs/main @@ -0,0 +1 @@ +b533fe4636f4a2507c08ddab40644d20b0006d6a \ No newline at end of file diff --git a/embeddings_cache/models--intfloat--e5-base/snapshots/b533fe4636f4a2507c08ddab40644d20b0006d6a/1_Pooling/config.json b/embeddings_cache/models--intfloat--e5-base/snapshots/b533fe4636f4a2507c08ddab40644d20b0006d6a/1_Pooling/config.json new file mode 100644 index 0000000000000000000000000000000000000000..2a824fa43c359360ecddb16808b539c6be892fef --- /dev/null +++ b/embeddings_cache/models--intfloat--e5-base/snapshots/b533fe4636f4a2507c08ddab40644d20b0006d6a/1_Pooling/config.json @@ -0,0 +1,7 @@ +{ + "word_embedding_dimension": 768, + "pooling_mode_cls_token": false, + "pooling_mode_mean_tokens": true, + "pooling_mode_max_tokens": false, + "pooling_mode_mean_sqrt_len_tokens": false +} \ No newline at end of file diff --git a/embeddings_cache/models--intfloat--e5-base/snapshots/b533fe4636f4a2507c08ddab40644d20b0006d6a/README.md b/embeddings_cache/models--intfloat--e5-base/snapshots/b533fe4636f4a2507c08ddab40644d20b0006d6a/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6e5e04267b1835d87865faa1efb256a63e7b6d83 --- /dev/null +++ b/embeddings_cache/models--intfloat--e5-base/snapshots/b533fe4636f4a2507c08ddab40644d20b0006d6a/README.md @@ -0,0 +1,2725 @@ +--- +tags: +- mteb +- Sentence Transformers +- sentence-similarity +- sentence-transformers +model-index: +- name: e5-base + results: + - task: + type: Classification + dataset: + type: mteb/amazon_counterfactual + name: MTEB AmazonCounterfactualClassification (en) + config: en + split: test + revision: e8379541af4e31359cca9fbcf4b00f2671dba205 + metrics: + - type: accuracy + value: 79.71641791044777 + - type: ap + value: 44.15426065428253 + - type: f1 + value: 73.89474407693241 + - task: + type: Classification + dataset: + type: mteb/amazon_polarity + name: MTEB AmazonPolarityClassification + config: default + split: test + revision: e2d317d38cd51312af73b3d32a06d1a08b442046 + metrics: + - type: accuracy + value: 87.9649 + - type: ap + value: 84.10171551915973 + - type: f1 + value: 87.94148377827356 + - task: + type: Classification + dataset: + type: mteb/amazon_reviews_multi + name: MTEB AmazonReviewsClassification (en) + config: en + split: test + revision: 1399c76144fd37290681b995c656ef9b2e06e26d + metrics: + - type: accuracy + value: 42.645999999999994 + - type: f1 + value: 42.230574673549 + - task: + type: Retrieval + dataset: + type: arguana + name: MTEB ArguAna + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 26.814 + - type: map_at_10 + value: 42.681999999999995 + - type: map_at_100 + value: 43.714 + - type: map_at_1000 + value: 43.724000000000004 + - type: map_at_3 + value: 38.11 + - type: map_at_5 + value: 40.666999999999994 + - type: mrr_at_1 + value: 27.168999999999997 + - type: mrr_at_10 + value: 42.84 + - type: mrr_at_100 + value: 43.864 + - type: mrr_at_1000 + value: 43.875 + - type: mrr_at_3 + value: 38.193 + - type: mrr_at_5 + value: 40.793 + - type: ndcg_at_1 + value: 26.814 + - type: ndcg_at_10 + value: 51.410999999999994 + - type: ndcg_at_100 + value: 55.713 + - type: ndcg_at_1000 + value: 55.957 + - type: ndcg_at_3 + value: 41.955 + - type: ndcg_at_5 + value: 46.558 + - type: precision_at_1 + value: 26.814 + - type: precision_at_10 + value: 7.922999999999999 + - type: precision_at_100 + value: 0.9780000000000001 + - type: precision_at_1000 + value: 0.1 + - type: precision_at_3 + value: 17.71 + - type: precision_at_5 + value: 12.859000000000002 + - type: recall_at_1 + value: 26.814 + - type: recall_at_10 + value: 79.232 + - type: recall_at_100 + value: 97.795 + - type: recall_at_1000 + value: 99.644 + - type: recall_at_3 + value: 53.129000000000005 + - type: recall_at_5 + value: 64.29599999999999 + - task: + type: Clustering + dataset: + type: mteb/arxiv-clustering-p2p + name: MTEB ArxivClusteringP2P + config: default + split: test + revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d + metrics: + - type: v_measure + value: 44.56933066536439 + - task: + type: Clustering + dataset: + type: mteb/arxiv-clustering-s2s + name: MTEB ArxivClusteringS2S + config: default + split: test + revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 + metrics: + - type: v_measure + value: 40.47647746165173 + - task: + type: Reranking + dataset: + type: mteb/askubuntudupquestions-reranking + name: MTEB AskUbuntuDupQuestions + config: default + split: test + revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 + metrics: + - type: map + value: 59.65675531567043 + - type: mrr + value: 72.95255683067317 + - task: + type: STS + dataset: + type: mteb/biosses-sts + name: MTEB BIOSSES + config: default + split: test + revision: d3fb88f8f02e40887cd149695127462bbcf29b4a + metrics: + - type: cos_sim_pearson + value: 85.83147014162338 + - type: cos_sim_spearman + value: 85.1031439521441 + - type: euclidean_pearson + value: 83.53609085510973 + - type: euclidean_spearman + value: 84.59650590202833 + - type: manhattan_pearson + value: 83.14611947586386 + - type: manhattan_spearman + value: 84.13384475757064 + - task: + type: Classification + dataset: + type: mteb/banking77 + name: MTEB Banking77Classification + config: default + split: test + revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 + metrics: + - type: accuracy + value: 83.32792207792208 + - type: f1 + value: 83.32037485050513 + - task: + type: Clustering + dataset: + type: mteb/biorxiv-clustering-p2p + name: MTEB BiorxivClusteringP2P + config: default + split: test + revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 + metrics: + - type: v_measure + value: 36.18605446588703 + - task: + type: Clustering + dataset: + type: mteb/biorxiv-clustering-s2s + name: MTEB BiorxivClusteringS2S + config: default + split: test + revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 + metrics: + - type: v_measure + value: 32.72379130181917 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackAndroidRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 30.659 + - type: map_at_10 + value: 40.333999999999996 + - type: map_at_100 + value: 41.763 + - type: map_at_1000 + value: 41.894 + - type: map_at_3 + value: 37.561 + - type: map_at_5 + value: 39.084 + - type: mrr_at_1 + value: 37.482 + - type: mrr_at_10 + value: 45.736 + - type: mrr_at_100 + value: 46.591 + - type: mrr_at_1000 + value: 46.644999999999996 + - type: mrr_at_3 + value: 43.491 + - type: mrr_at_5 + value: 44.75 + - type: ndcg_at_1 + value: 37.482 + - type: ndcg_at_10 + value: 45.606 + - type: ndcg_at_100 + value: 51.172 + - type: ndcg_at_1000 + value: 53.407000000000004 + - type: ndcg_at_3 + value: 41.808 + - type: ndcg_at_5 + value: 43.449 + - type: precision_at_1 + value: 37.482 + - type: precision_at_10 + value: 8.254999999999999 + - type: precision_at_100 + value: 1.3719999999999999 + - type: precision_at_1000 + value: 0.186 + - type: precision_at_3 + value: 19.695 + - type: precision_at_5 + value: 13.847999999999999 + - type: recall_at_1 + value: 30.659 + - type: recall_at_10 + value: 55.409 + - type: recall_at_100 + value: 78.687 + - type: recall_at_1000 + value: 93.068 + - type: recall_at_3 + value: 43.891999999999996 + - type: recall_at_5 + value: 48.678 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackEnglishRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 30.977 + - type: map_at_10 + value: 40.296 + - type: map_at_100 + value: 41.453 + - type: map_at_1000 + value: 41.581 + - type: map_at_3 + value: 37.619 + - type: map_at_5 + value: 39.181 + - type: mrr_at_1 + value: 39.108 + - type: mrr_at_10 + value: 46.894000000000005 + - type: mrr_at_100 + value: 47.55 + - type: mrr_at_1000 + value: 47.598 + - type: mrr_at_3 + value: 44.766 + - type: mrr_at_5 + value: 46.062999999999995 + - type: ndcg_at_1 + value: 39.108 + - type: ndcg_at_10 + value: 45.717 + - type: ndcg_at_100 + value: 49.941 + - type: ndcg_at_1000 + value: 52.138 + - type: ndcg_at_3 + value: 42.05 + - type: ndcg_at_5 + value: 43.893 + - type: precision_at_1 + value: 39.108 + - type: precision_at_10 + value: 8.306 + - type: precision_at_100 + value: 1.3419999999999999 + - type: precision_at_1000 + value: 0.184 + - type: precision_at_3 + value: 19.979 + - type: precision_at_5 + value: 14.038 + - type: recall_at_1 + value: 30.977 + - type: recall_at_10 + value: 54.688 + - type: recall_at_100 + value: 72.556 + - type: recall_at_1000 + value: 86.53800000000001 + - type: recall_at_3 + value: 43.388 + - type: recall_at_5 + value: 48.717 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackGamingRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 39.812 + - type: map_at_10 + value: 50.1 + - type: map_at_100 + value: 51.193999999999996 + - type: map_at_1000 + value: 51.258 + - type: map_at_3 + value: 47.510999999999996 + - type: map_at_5 + value: 48.891 + - type: mrr_at_1 + value: 45.266 + - type: mrr_at_10 + value: 53.459999999999994 + - type: mrr_at_100 + value: 54.19199999999999 + - type: mrr_at_1000 + value: 54.228 + - type: mrr_at_3 + value: 51.296 + - type: mrr_at_5 + value: 52.495999999999995 + - type: ndcg_at_1 + value: 45.266 + - type: ndcg_at_10 + value: 55.034000000000006 + - type: ndcg_at_100 + value: 59.458 + - type: ndcg_at_1000 + value: 60.862 + - type: ndcg_at_3 + value: 50.52799999999999 + - type: ndcg_at_5 + value: 52.564 + - type: precision_at_1 + value: 45.266 + - type: precision_at_10 + value: 8.483 + - type: precision_at_100 + value: 1.162 + - type: precision_at_1000 + value: 0.133 + - type: precision_at_3 + value: 21.944 + - type: precision_at_5 + value: 14.721 + - type: recall_at_1 + value: 39.812 + - type: recall_at_10 + value: 66.36 + - type: recall_at_100 + value: 85.392 + - type: recall_at_1000 + value: 95.523 + - type: recall_at_3 + value: 54.127 + - type: recall_at_5 + value: 59.245000000000005 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackGisRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 26.186 + - type: map_at_10 + value: 33.18 + - type: map_at_100 + value: 34.052 + - type: map_at_1000 + value: 34.149 + - type: map_at_3 + value: 31.029 + - type: map_at_5 + value: 32.321 + - type: mrr_at_1 + value: 28.136 + - type: mrr_at_10 + value: 35.195 + - type: mrr_at_100 + value: 35.996 + - type: mrr_at_1000 + value: 36.076 + - type: mrr_at_3 + value: 33.051 + - type: mrr_at_5 + value: 34.407 + - type: ndcg_at_1 + value: 28.136 + - type: ndcg_at_10 + value: 37.275999999999996 + - type: ndcg_at_100 + value: 41.935 + - type: ndcg_at_1000 + value: 44.389 + - type: ndcg_at_3 + value: 33.059 + - type: ndcg_at_5 + value: 35.313 + - type: precision_at_1 + value: 28.136 + - type: precision_at_10 + value: 5.457999999999999 + - type: precision_at_100 + value: 0.826 + - type: precision_at_1000 + value: 0.107 + - type: precision_at_3 + value: 13.522 + - type: precision_at_5 + value: 9.424000000000001 + - type: recall_at_1 + value: 26.186 + - type: recall_at_10 + value: 47.961999999999996 + - type: recall_at_100 + value: 70.072 + - type: recall_at_1000 + value: 88.505 + - type: recall_at_3 + value: 36.752 + - type: recall_at_5 + value: 42.168 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackMathematicaRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 16.586000000000002 + - type: map_at_10 + value: 23.637 + - type: map_at_100 + value: 24.82 + - type: map_at_1000 + value: 24.95 + - type: map_at_3 + value: 21.428 + - type: map_at_5 + value: 22.555 + - type: mrr_at_1 + value: 20.771 + - type: mrr_at_10 + value: 27.839999999999996 + - type: mrr_at_100 + value: 28.887 + - type: mrr_at_1000 + value: 28.967 + - type: mrr_at_3 + value: 25.56 + - type: mrr_at_5 + value: 26.723000000000003 + - type: ndcg_at_1 + value: 20.771 + - type: ndcg_at_10 + value: 28.255000000000003 + - type: ndcg_at_100 + value: 33.886 + - type: ndcg_at_1000 + value: 36.963 + - type: ndcg_at_3 + value: 24.056 + - type: ndcg_at_5 + value: 25.818 + - type: precision_at_1 + value: 20.771 + - type: precision_at_10 + value: 5.1 + - type: precision_at_100 + value: 0.9119999999999999 + - type: precision_at_1000 + value: 0.132 + - type: precision_at_3 + value: 11.526 + - type: precision_at_5 + value: 8.158999999999999 + - type: recall_at_1 + value: 16.586000000000002 + - type: recall_at_10 + value: 38.456 + - type: recall_at_100 + value: 62.666 + - type: recall_at_1000 + value: 84.47 + - type: recall_at_3 + value: 26.765 + - type: recall_at_5 + value: 31.297000000000004 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackPhysicsRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 28.831 + - type: map_at_10 + value: 37.545 + - type: map_at_100 + value: 38.934999999999995 + - type: map_at_1000 + value: 39.044000000000004 + - type: map_at_3 + value: 34.601 + - type: map_at_5 + value: 36.302 + - type: mrr_at_1 + value: 34.264 + - type: mrr_at_10 + value: 42.569 + - type: mrr_at_100 + value: 43.514 + - type: mrr_at_1000 + value: 43.561 + - type: mrr_at_3 + value: 40.167 + - type: mrr_at_5 + value: 41.678 + - type: ndcg_at_1 + value: 34.264 + - type: ndcg_at_10 + value: 42.914 + - type: ndcg_at_100 + value: 48.931999999999995 + - type: ndcg_at_1000 + value: 51.004000000000005 + - type: ndcg_at_3 + value: 38.096999999999994 + - type: ndcg_at_5 + value: 40.509 + - type: precision_at_1 + value: 34.264 + - type: precision_at_10 + value: 7.642 + - type: precision_at_100 + value: 1.258 + - type: precision_at_1000 + value: 0.161 + - type: precision_at_3 + value: 17.453 + - type: precision_at_5 + value: 12.608 + - type: recall_at_1 + value: 28.831 + - type: recall_at_10 + value: 53.56999999999999 + - type: recall_at_100 + value: 79.26100000000001 + - type: recall_at_1000 + value: 92.862 + - type: recall_at_3 + value: 40.681 + - type: recall_at_5 + value: 46.597 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackProgrammersRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 27.461000000000002 + - type: map_at_10 + value: 35.885 + - type: map_at_100 + value: 37.039 + - type: map_at_1000 + value: 37.16 + - type: map_at_3 + value: 33.451 + - type: map_at_5 + value: 34.807 + - type: mrr_at_1 + value: 34.018 + - type: mrr_at_10 + value: 41.32 + - type: mrr_at_100 + value: 42.157 + - type: mrr_at_1000 + value: 42.223 + - type: mrr_at_3 + value: 39.288000000000004 + - type: mrr_at_5 + value: 40.481 + - type: ndcg_at_1 + value: 34.018 + - type: ndcg_at_10 + value: 40.821000000000005 + - type: ndcg_at_100 + value: 46.053 + - type: ndcg_at_1000 + value: 48.673 + - type: ndcg_at_3 + value: 36.839 + - type: ndcg_at_5 + value: 38.683 + - type: precision_at_1 + value: 34.018 + - type: precision_at_10 + value: 7.009 + - type: precision_at_100 + value: 1.123 + - type: precision_at_1000 + value: 0.153 + - type: precision_at_3 + value: 16.933 + - type: precision_at_5 + value: 11.826 + - type: recall_at_1 + value: 27.461000000000002 + - type: recall_at_10 + value: 50.285000000000004 + - type: recall_at_100 + value: 73.25500000000001 + - type: recall_at_1000 + value: 91.17699999999999 + - type: recall_at_3 + value: 39.104 + - type: recall_at_5 + value: 43.968 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 26.980083333333337 + - type: map_at_10 + value: 34.47208333333333 + - type: map_at_100 + value: 35.609249999999996 + - type: map_at_1000 + value: 35.72833333333333 + - type: map_at_3 + value: 32.189416666666666 + - type: map_at_5 + value: 33.44683333333334 + - type: mrr_at_1 + value: 31.731666666666662 + - type: mrr_at_10 + value: 38.518 + - type: mrr_at_100 + value: 39.38166666666667 + - type: mrr_at_1000 + value: 39.446999999999996 + - type: mrr_at_3 + value: 36.49966666666668 + - type: mrr_at_5 + value: 37.639916666666664 + - type: ndcg_at_1 + value: 31.731666666666662 + - type: ndcg_at_10 + value: 38.92033333333333 + - type: ndcg_at_100 + value: 44.01675 + - type: ndcg_at_1000 + value: 46.51075 + - type: ndcg_at_3 + value: 35.09766666666667 + - type: ndcg_at_5 + value: 36.842999999999996 + - type: precision_at_1 + value: 31.731666666666662 + - type: precision_at_10 + value: 6.472583333333332 + - type: precision_at_100 + value: 1.0665 + - type: precision_at_1000 + value: 0.14725000000000002 + - type: precision_at_3 + value: 15.659083333333331 + - type: precision_at_5 + value: 10.878833333333333 + - type: recall_at_1 + value: 26.980083333333337 + - type: recall_at_10 + value: 48.13925 + - type: recall_at_100 + value: 70.70149999999998 + - type: recall_at_1000 + value: 88.10775000000001 + - type: recall_at_3 + value: 37.30091666666667 + - type: recall_at_5 + value: 41.90358333333333 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackStatsRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 25.607999999999997 + - type: map_at_10 + value: 30.523 + - type: map_at_100 + value: 31.409 + - type: map_at_1000 + value: 31.507 + - type: map_at_3 + value: 28.915000000000003 + - type: map_at_5 + value: 29.756 + - type: mrr_at_1 + value: 28.681 + - type: mrr_at_10 + value: 33.409 + - type: mrr_at_100 + value: 34.241 + - type: mrr_at_1000 + value: 34.313 + - type: mrr_at_3 + value: 32.029999999999994 + - type: mrr_at_5 + value: 32.712 + - type: ndcg_at_1 + value: 28.681 + - type: ndcg_at_10 + value: 33.733000000000004 + - type: ndcg_at_100 + value: 38.32 + - type: ndcg_at_1000 + value: 40.937 + - type: ndcg_at_3 + value: 30.898999999999997 + - type: ndcg_at_5 + value: 32.088 + - type: precision_at_1 + value: 28.681 + - type: precision_at_10 + value: 4.968999999999999 + - type: precision_at_100 + value: 0.79 + - type: precision_at_1000 + value: 0.11 + - type: precision_at_3 + value: 12.73 + - type: precision_at_5 + value: 8.558 + - type: recall_at_1 + value: 25.607999999999997 + - type: recall_at_10 + value: 40.722 + - type: recall_at_100 + value: 61.956999999999994 + - type: recall_at_1000 + value: 81.43 + - type: recall_at_3 + value: 32.785 + - type: recall_at_5 + value: 35.855 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackTexRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 20.399 + - type: map_at_10 + value: 25.968000000000004 + - type: map_at_100 + value: 26.985999999999997 + - type: map_at_1000 + value: 27.105 + - type: map_at_3 + value: 24.215 + - type: map_at_5 + value: 25.157 + - type: mrr_at_1 + value: 24.708 + - type: mrr_at_10 + value: 29.971999999999998 + - type: mrr_at_100 + value: 30.858 + - type: mrr_at_1000 + value: 30.934 + - type: mrr_at_3 + value: 28.304000000000002 + - type: mrr_at_5 + value: 29.183999999999997 + - type: ndcg_at_1 + value: 24.708 + - type: ndcg_at_10 + value: 29.676000000000002 + - type: ndcg_at_100 + value: 34.656 + - type: ndcg_at_1000 + value: 37.588 + - type: ndcg_at_3 + value: 26.613 + - type: ndcg_at_5 + value: 27.919 + - type: precision_at_1 + value: 24.708 + - type: precision_at_10 + value: 5.01 + - type: precision_at_100 + value: 0.876 + - type: precision_at_1000 + value: 0.13 + - type: precision_at_3 + value: 11.975 + - type: precision_at_5 + value: 8.279 + - type: recall_at_1 + value: 20.399 + - type: recall_at_10 + value: 36.935 + - type: recall_at_100 + value: 59.532 + - type: recall_at_1000 + value: 80.58 + - type: recall_at_3 + value: 27.979 + - type: recall_at_5 + value: 31.636999999999997 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackUnixRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 27.606 + - type: map_at_10 + value: 34.213 + - type: map_at_100 + value: 35.339999999999996 + - type: map_at_1000 + value: 35.458 + - type: map_at_3 + value: 31.987 + - type: map_at_5 + value: 33.322 + - type: mrr_at_1 + value: 31.53 + - type: mrr_at_10 + value: 37.911 + - type: mrr_at_100 + value: 38.879000000000005 + - type: mrr_at_1000 + value: 38.956 + - type: mrr_at_3 + value: 35.868 + - type: mrr_at_5 + value: 37.047999999999995 + - type: ndcg_at_1 + value: 31.53 + - type: ndcg_at_10 + value: 38.312000000000005 + - type: ndcg_at_100 + value: 43.812 + - type: ndcg_at_1000 + value: 46.414 + - type: ndcg_at_3 + value: 34.319 + - type: ndcg_at_5 + value: 36.312 + - type: precision_at_1 + value: 31.53 + - type: precision_at_10 + value: 5.970000000000001 + - type: precision_at_100 + value: 0.9939999999999999 + - type: precision_at_1000 + value: 0.133 + - type: precision_at_3 + value: 14.738999999999999 + - type: precision_at_5 + value: 10.242999999999999 + - type: recall_at_1 + value: 27.606 + - type: recall_at_10 + value: 47.136 + - type: recall_at_100 + value: 71.253 + - type: recall_at_1000 + value: 89.39399999999999 + - type: recall_at_3 + value: 36.342 + - type: recall_at_5 + value: 41.388999999999996 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackWebmastersRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 24.855 + - type: map_at_10 + value: 31.963 + - type: map_at_100 + value: 33.371 + - type: map_at_1000 + value: 33.584 + - type: map_at_3 + value: 29.543999999999997 + - type: map_at_5 + value: 30.793 + - type: mrr_at_1 + value: 29.644 + - type: mrr_at_10 + value: 35.601 + - type: mrr_at_100 + value: 36.551 + - type: mrr_at_1000 + value: 36.623 + - type: mrr_at_3 + value: 33.399 + - type: mrr_at_5 + value: 34.575 + - type: ndcg_at_1 + value: 29.644 + - type: ndcg_at_10 + value: 36.521 + - type: ndcg_at_100 + value: 42.087 + - type: ndcg_at_1000 + value: 45.119 + - type: ndcg_at_3 + value: 32.797 + - type: ndcg_at_5 + value: 34.208 + - type: precision_at_1 + value: 29.644 + - type: precision_at_10 + value: 6.7 + - type: precision_at_100 + value: 1.374 + - type: precision_at_1000 + value: 0.22899999999999998 + - type: precision_at_3 + value: 15.152 + - type: precision_at_5 + value: 10.671999999999999 + - type: recall_at_1 + value: 24.855 + - type: recall_at_10 + value: 45.449 + - type: recall_at_100 + value: 70.921 + - type: recall_at_1000 + value: 90.629 + - type: recall_at_3 + value: 33.526 + - type: recall_at_5 + value: 37.848 + - task: + type: Retrieval + dataset: + type: BeIR/cqadupstack + name: MTEB CQADupstackWordpressRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 24.781 + - type: map_at_10 + value: 30.020999999999997 + - type: map_at_100 + value: 30.948999999999998 + - type: map_at_1000 + value: 31.05 + - type: map_at_3 + value: 28.412 + - type: map_at_5 + value: 29.193 + - type: mrr_at_1 + value: 27.172 + - type: mrr_at_10 + value: 32.309 + - type: mrr_at_100 + value: 33.164 + - type: mrr_at_1000 + value: 33.239999999999995 + - type: mrr_at_3 + value: 30.775999999999996 + - type: mrr_at_5 + value: 31.562 + - type: ndcg_at_1 + value: 27.172 + - type: ndcg_at_10 + value: 33.178999999999995 + - type: ndcg_at_100 + value: 37.949 + - type: ndcg_at_1000 + value: 40.635 + - type: ndcg_at_3 + value: 30.107 + - type: ndcg_at_5 + value: 31.36 + - type: precision_at_1 + value: 27.172 + - type: precision_at_10 + value: 4.769 + - type: precision_at_100 + value: 0.769 + - type: precision_at_1000 + value: 0.109 + - type: precision_at_3 + value: 12.261 + - type: precision_at_5 + value: 8.17 + - type: recall_at_1 + value: 24.781 + - type: recall_at_10 + value: 40.699000000000005 + - type: recall_at_100 + value: 62.866 + - type: recall_at_1000 + value: 83.11699999999999 + - type: recall_at_3 + value: 32.269999999999996 + - type: recall_at_5 + value: 35.443999999999996 + - task: + type: Retrieval + dataset: + type: climate-fever + name: MTEB ClimateFEVER + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 5.2139999999999995 + - type: map_at_10 + value: 9.986 + - type: map_at_100 + value: 11.343 + - type: map_at_1000 + value: 11.55 + - type: map_at_3 + value: 7.961 + - type: map_at_5 + value: 8.967 + - type: mrr_at_1 + value: 12.052 + - type: mrr_at_10 + value: 20.165 + - type: mrr_at_100 + value: 21.317 + - type: mrr_at_1000 + value: 21.399 + - type: mrr_at_3 + value: 17.079 + - type: mrr_at_5 + value: 18.695 + - type: ndcg_at_1 + value: 12.052 + - type: ndcg_at_10 + value: 15.375 + - type: ndcg_at_100 + value: 21.858 + - type: ndcg_at_1000 + value: 26.145000000000003 + - type: ndcg_at_3 + value: 11.334 + - type: ndcg_at_5 + value: 12.798000000000002 + - type: precision_at_1 + value: 12.052 + - type: precision_at_10 + value: 5.16 + - type: precision_at_100 + value: 1.206 + - type: precision_at_1000 + value: 0.198 + - type: precision_at_3 + value: 8.73 + - type: precision_at_5 + value: 7.114 + - type: recall_at_1 + value: 5.2139999999999995 + - type: recall_at_10 + value: 20.669999999999998 + - type: recall_at_100 + value: 43.901 + - type: recall_at_1000 + value: 68.447 + - type: recall_at_3 + value: 11.049000000000001 + - type: recall_at_5 + value: 14.652999999999999 + - task: + type: Retrieval + dataset: + type: dbpedia-entity + name: MTEB DBPedia + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 8.511000000000001 + - type: map_at_10 + value: 19.503 + - type: map_at_100 + value: 27.46 + - type: map_at_1000 + value: 29.187 + - type: map_at_3 + value: 14.030999999999999 + - type: map_at_5 + value: 16.329 + - type: mrr_at_1 + value: 63.74999999999999 + - type: mrr_at_10 + value: 73.419 + - type: mrr_at_100 + value: 73.691 + - type: mrr_at_1000 + value: 73.697 + - type: mrr_at_3 + value: 71.792 + - type: mrr_at_5 + value: 72.979 + - type: ndcg_at_1 + value: 53.125 + - type: ndcg_at_10 + value: 41.02 + - type: ndcg_at_100 + value: 45.407 + - type: ndcg_at_1000 + value: 52.68000000000001 + - type: ndcg_at_3 + value: 46.088 + - type: ndcg_at_5 + value: 43.236000000000004 + - type: precision_at_1 + value: 63.74999999999999 + - type: precision_at_10 + value: 32.35 + - type: precision_at_100 + value: 10.363 + - type: precision_at_1000 + value: 2.18 + - type: precision_at_3 + value: 49.667 + - type: precision_at_5 + value: 41.5 + - type: recall_at_1 + value: 8.511000000000001 + - type: recall_at_10 + value: 24.851 + - type: recall_at_100 + value: 50.745 + - type: recall_at_1000 + value: 73.265 + - type: recall_at_3 + value: 15.716 + - type: recall_at_5 + value: 19.256 + - task: + type: Classification + dataset: + type: mteb/emotion + name: MTEB EmotionClassification + config: default + split: test + revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 + metrics: + - type: accuracy + value: 49.43500000000001 + - type: f1 + value: 44.56288273966374 + - task: + type: Retrieval + dataset: + type: fever + name: MTEB FEVER + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 40.858 + - type: map_at_10 + value: 52.276 + - type: map_at_100 + value: 52.928 + - type: map_at_1000 + value: 52.966 + - type: map_at_3 + value: 49.729 + - type: map_at_5 + value: 51.27 + - type: mrr_at_1 + value: 43.624 + - type: mrr_at_10 + value: 55.22899999999999 + - type: mrr_at_100 + value: 55.823 + - type: mrr_at_1000 + value: 55.85 + - type: mrr_at_3 + value: 52.739999999999995 + - type: mrr_at_5 + value: 54.251000000000005 + - type: ndcg_at_1 + value: 43.624 + - type: ndcg_at_10 + value: 58.23500000000001 + - type: ndcg_at_100 + value: 61.315 + - type: ndcg_at_1000 + value: 62.20099999999999 + - type: ndcg_at_3 + value: 53.22 + - type: ndcg_at_5 + value: 55.88999999999999 + - type: precision_at_1 + value: 43.624 + - type: precision_at_10 + value: 8.068999999999999 + - type: precision_at_100 + value: 0.975 + - type: precision_at_1000 + value: 0.107 + - type: precision_at_3 + value: 21.752 + - type: precision_at_5 + value: 14.515 + - type: recall_at_1 + value: 40.858 + - type: recall_at_10 + value: 73.744 + - type: recall_at_100 + value: 87.667 + - type: recall_at_1000 + value: 94.15599999999999 + - type: recall_at_3 + value: 60.287 + - type: recall_at_5 + value: 66.703 + - task: + type: Retrieval + dataset: + type: fiqa + name: MTEB FiQA2018 + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 17.864 + - type: map_at_10 + value: 28.592000000000002 + - type: map_at_100 + value: 30.165 + - type: map_at_1000 + value: 30.364 + - type: map_at_3 + value: 24.586 + - type: map_at_5 + value: 26.717000000000002 + - type: mrr_at_1 + value: 35.031 + - type: mrr_at_10 + value: 43.876 + - type: mrr_at_100 + value: 44.683 + - type: mrr_at_1000 + value: 44.736 + - type: mrr_at_3 + value: 40.998000000000005 + - type: mrr_at_5 + value: 42.595 + - type: ndcg_at_1 + value: 35.031 + - type: ndcg_at_10 + value: 36.368 + - type: ndcg_at_100 + value: 42.472 + - type: ndcg_at_1000 + value: 45.973000000000006 + - type: ndcg_at_3 + value: 31.915 + - type: ndcg_at_5 + value: 33.394 + - type: precision_at_1 + value: 35.031 + - type: precision_at_10 + value: 10.139 + - type: precision_at_100 + value: 1.6420000000000001 + - type: precision_at_1000 + value: 0.22699999999999998 + - type: precision_at_3 + value: 21.142 + - type: precision_at_5 + value: 15.772 + - type: recall_at_1 + value: 17.864 + - type: recall_at_10 + value: 43.991 + - type: recall_at_100 + value: 66.796 + - type: recall_at_1000 + value: 87.64 + - type: recall_at_3 + value: 28.915999999999997 + - type: recall_at_5 + value: 35.185 + - task: + type: Retrieval + dataset: + type: hotpotqa + name: MTEB HotpotQA + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 36.556 + - type: map_at_10 + value: 53.056000000000004 + - type: map_at_100 + value: 53.909 + - type: map_at_1000 + value: 53.98 + - type: map_at_3 + value: 49.982 + - type: map_at_5 + value: 51.9 + - type: mrr_at_1 + value: 73.113 + - type: mrr_at_10 + value: 79.381 + - type: mrr_at_100 + value: 79.60300000000001 + - type: mrr_at_1000 + value: 79.617 + - type: mrr_at_3 + value: 78.298 + - type: mrr_at_5 + value: 78.995 + - type: ndcg_at_1 + value: 73.113 + - type: ndcg_at_10 + value: 62.21 + - type: ndcg_at_100 + value: 65.242 + - type: ndcg_at_1000 + value: 66.667 + - type: ndcg_at_3 + value: 57.717 + - type: ndcg_at_5 + value: 60.224 + - type: precision_at_1 + value: 73.113 + - type: precision_at_10 + value: 12.842999999999998 + - type: precision_at_100 + value: 1.522 + - type: precision_at_1000 + value: 0.17099999999999999 + - type: precision_at_3 + value: 36.178 + - type: precision_at_5 + value: 23.695 + - type: recall_at_1 + value: 36.556 + - type: recall_at_10 + value: 64.213 + - type: recall_at_100 + value: 76.077 + - type: recall_at_1000 + value: 85.53699999999999 + - type: recall_at_3 + value: 54.266999999999996 + - type: recall_at_5 + value: 59.236999999999995 + - task: + type: Classification + dataset: + type: mteb/imdb + name: MTEB ImdbClassification + config: default + split: test + revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 + metrics: + - type: accuracy + value: 75.958 + - type: ap + value: 69.82869527654348 + - type: f1 + value: 75.89120903005633 + - task: + type: Retrieval + dataset: + type: msmarco + name: MTEB MSMARCO + config: default + split: dev + revision: None + metrics: + - type: map_at_1 + value: 23.608 + - type: map_at_10 + value: 36.144 + - type: map_at_100 + value: 37.244 + - type: map_at_1000 + value: 37.291999999999994 + - type: map_at_3 + value: 32.287 + - type: map_at_5 + value: 34.473 + - type: mrr_at_1 + value: 24.226 + - type: mrr_at_10 + value: 36.711 + - type: mrr_at_100 + value: 37.758 + - type: mrr_at_1000 + value: 37.8 + - type: mrr_at_3 + value: 32.92 + - type: mrr_at_5 + value: 35.104 + - type: ndcg_at_1 + value: 24.269 + - type: ndcg_at_10 + value: 43.138 + - type: ndcg_at_100 + value: 48.421 + - type: ndcg_at_1000 + value: 49.592000000000006 + - type: ndcg_at_3 + value: 35.269 + - type: ndcg_at_5 + value: 39.175 + - type: precision_at_1 + value: 24.269 + - type: precision_at_10 + value: 6.755999999999999 + - type: precision_at_100 + value: 0.941 + - type: precision_at_1000 + value: 0.104 + - type: precision_at_3 + value: 14.938 + - type: precision_at_5 + value: 10.934000000000001 + - type: recall_at_1 + value: 23.608 + - type: recall_at_10 + value: 64.679 + - type: recall_at_100 + value: 89.027 + - type: recall_at_1000 + value: 97.91 + - type: recall_at_3 + value: 43.25 + - type: recall_at_5 + value: 52.617000000000004 + - task: + type: Classification + dataset: + type: mteb/mtop_domain + name: MTEB MTOPDomainClassification (en) + config: en + split: test + revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf + metrics: + - type: accuracy + value: 93.21477428180576 + - type: f1 + value: 92.92502305092152 + - task: + type: Classification + dataset: + type: mteb/mtop_intent + name: MTEB MTOPIntentClassification (en) + config: en + split: test + revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba + metrics: + - type: accuracy + value: 74.76744186046511 + - type: f1 + value: 59.19855520057899 + - task: + type: Classification + dataset: + type: mteb/amazon_massive_intent + name: MTEB MassiveIntentClassification (en) + config: en + split: test + revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 + metrics: + - type: accuracy + value: 72.24613315400134 + - type: f1 + value: 70.19950395651232 + - task: + type: Classification + dataset: + type: mteb/amazon_massive_scenario + name: MTEB MassiveScenarioClassification (en) + config: en + split: test + revision: 7d571f92784cd94a019292a1f45445077d0ef634 + metrics: + - type: accuracy + value: 76.75857431069268 + - type: f1 + value: 76.5433450230191 + - task: + type: Clustering + dataset: + type: mteb/medrxiv-clustering-p2p + name: MTEB MedrxivClusteringP2P + config: default + split: test + revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 + metrics: + - type: v_measure + value: 31.525463791623604 + - task: + type: Clustering + dataset: + type: mteb/medrxiv-clustering-s2s + name: MTEB MedrxivClusteringS2S + config: default + split: test + revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 + metrics: + - type: v_measure + value: 28.28695907385136 + - task: + type: Reranking + dataset: + type: mteb/mind_small + name: MTEB MindSmallReranking + config: default + split: test + revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 + metrics: + - type: map + value: 30.068174046665224 + - type: mrr + value: 30.827586642840803 + - task: + type: Retrieval + dataset: + type: nfcorpus + name: MTEB NFCorpus + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 6.322 + - type: map_at_10 + value: 13.919999999999998 + - type: map_at_100 + value: 17.416 + - type: map_at_1000 + value: 18.836 + - type: map_at_3 + value: 10.111 + - type: map_at_5 + value: 11.991999999999999 + - type: mrr_at_1 + value: 48.297000000000004 + - type: mrr_at_10 + value: 57.114 + - type: mrr_at_100 + value: 57.713 + - type: mrr_at_1000 + value: 57.751 + - type: mrr_at_3 + value: 55.108000000000004 + - type: mrr_at_5 + value: 56.533 + - type: ndcg_at_1 + value: 46.44 + - type: ndcg_at_10 + value: 36.589 + - type: ndcg_at_100 + value: 33.202 + - type: ndcg_at_1000 + value: 41.668 + - type: ndcg_at_3 + value: 41.302 + - type: ndcg_at_5 + value: 39.829 + - type: precision_at_1 + value: 47.988 + - type: precision_at_10 + value: 27.059 + - type: precision_at_100 + value: 8.235000000000001 + - type: precision_at_1000 + value: 2.091 + - type: precision_at_3 + value: 38.184000000000005 + - type: precision_at_5 + value: 34.365 + - type: recall_at_1 + value: 6.322 + - type: recall_at_10 + value: 18.288 + - type: recall_at_100 + value: 32.580999999999996 + - type: recall_at_1000 + value: 63.605999999999995 + - type: recall_at_3 + value: 11.266 + - type: recall_at_5 + value: 14.69 + - task: + type: Retrieval + dataset: + type: nq + name: MTEB NQ + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 36.586999999999996 + - type: map_at_10 + value: 52.464 + - type: map_at_100 + value: 53.384 + - type: map_at_1000 + value: 53.405 + - type: map_at_3 + value: 48.408 + - type: map_at_5 + value: 50.788999999999994 + - type: mrr_at_1 + value: 40.904 + - type: mrr_at_10 + value: 54.974000000000004 + - type: mrr_at_100 + value: 55.60699999999999 + - type: mrr_at_1000 + value: 55.623 + - type: mrr_at_3 + value: 51.73799999999999 + - type: mrr_at_5 + value: 53.638 + - type: ndcg_at_1 + value: 40.904 + - type: ndcg_at_10 + value: 59.965999999999994 + - type: ndcg_at_100 + value: 63.613 + - type: ndcg_at_1000 + value: 64.064 + - type: ndcg_at_3 + value: 52.486 + - type: ndcg_at_5 + value: 56.377 + - type: precision_at_1 + value: 40.904 + - type: precision_at_10 + value: 9.551 + - type: precision_at_100 + value: 1.162 + - type: precision_at_1000 + value: 0.12 + - type: precision_at_3 + value: 23.552 + - type: precision_at_5 + value: 16.436999999999998 + - type: recall_at_1 + value: 36.586999999999996 + - type: recall_at_10 + value: 80.094 + - type: recall_at_100 + value: 95.515 + - type: recall_at_1000 + value: 98.803 + - type: recall_at_3 + value: 60.907 + - type: recall_at_5 + value: 69.817 + - task: + type: Retrieval + dataset: + type: quora + name: MTEB QuoraRetrieval + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 70.422 + - type: map_at_10 + value: 84.113 + - type: map_at_100 + value: 84.744 + - type: map_at_1000 + value: 84.762 + - type: map_at_3 + value: 81.171 + - type: map_at_5 + value: 83.039 + - type: mrr_at_1 + value: 81.12 + - type: mrr_at_10 + value: 87.277 + - type: mrr_at_100 + value: 87.384 + - type: mrr_at_1000 + value: 87.385 + - type: mrr_at_3 + value: 86.315 + - type: mrr_at_5 + value: 86.981 + - type: ndcg_at_1 + value: 81.12 + - type: ndcg_at_10 + value: 87.92 + - type: ndcg_at_100 + value: 89.178 + - type: ndcg_at_1000 + value: 89.29899999999999 + - type: ndcg_at_3 + value: 85.076 + - type: ndcg_at_5 + value: 86.67099999999999 + - type: precision_at_1 + value: 81.12 + - type: precision_at_10 + value: 13.325999999999999 + - type: precision_at_100 + value: 1.524 + - type: precision_at_1000 + value: 0.157 + - type: precision_at_3 + value: 37.16 + - type: precision_at_5 + value: 24.456 + - type: recall_at_1 + value: 70.422 + - type: recall_at_10 + value: 95.00800000000001 + - type: recall_at_100 + value: 99.38 + - type: recall_at_1000 + value: 99.94800000000001 + - type: recall_at_3 + value: 86.809 + - type: recall_at_5 + value: 91.334 + - task: + type: Clustering + dataset: + type: mteb/reddit-clustering + name: MTEB RedditClustering + config: default + split: test + revision: 24640382cdbf8abc73003fb0fa6d111a705499eb + metrics: + - type: v_measure + value: 48.18491891699636 + - task: + type: Clustering + dataset: + type: mteb/reddit-clustering-p2p + name: MTEB RedditClusteringP2P + config: default + split: test + revision: 282350215ef01743dc01b456c7f5241fa8937f16 + metrics: + - type: v_measure + value: 62.190639679711914 + - task: + type: Retrieval + dataset: + type: scidocs + name: MTEB SCIDOCS + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 4.478 + - type: map_at_10 + value: 11.268 + - type: map_at_100 + value: 13.129 + - type: map_at_1000 + value: 13.41 + - type: map_at_3 + value: 8.103 + - type: map_at_5 + value: 9.609 + - type: mrr_at_1 + value: 22 + - type: mrr_at_10 + value: 32.248 + - type: mrr_at_100 + value: 33.355000000000004 + - type: mrr_at_1000 + value: 33.42 + - type: mrr_at_3 + value: 29.15 + - type: mrr_at_5 + value: 30.785 + - type: ndcg_at_1 + value: 22 + - type: ndcg_at_10 + value: 18.990000000000002 + - type: ndcg_at_100 + value: 26.302999999999997 + - type: ndcg_at_1000 + value: 31.537 + - type: ndcg_at_3 + value: 18.034 + - type: ndcg_at_5 + value: 15.655 + - type: precision_at_1 + value: 22 + - type: precision_at_10 + value: 9.91 + - type: precision_at_100 + value: 2.0420000000000003 + - type: precision_at_1000 + value: 0.33 + - type: precision_at_3 + value: 16.933 + - type: precision_at_5 + value: 13.719999999999999 + - type: recall_at_1 + value: 4.478 + - type: recall_at_10 + value: 20.087 + - type: recall_at_100 + value: 41.457 + - type: recall_at_1000 + value: 67.10199999999999 + - type: recall_at_3 + value: 10.313 + - type: recall_at_5 + value: 13.927999999999999 + - task: + type: STS + dataset: + type: mteb/sickr-sts + name: MTEB SICK-R + config: default + split: test + revision: a6ea5a8cab320b040a23452cc28066d9beae2cee + metrics: + - type: cos_sim_pearson + value: 84.27341574565806 + - type: cos_sim_spearman + value: 79.66419880841734 + - type: euclidean_pearson + value: 81.32473321838208 + - type: euclidean_spearman + value: 79.29828832085133 + - type: manhattan_pearson + value: 81.25554065883132 + - type: manhattan_spearman + value: 79.23275543279853 + - task: + type: STS + dataset: + type: mteb/sts12-sts + name: MTEB STS12 + config: default + split: test + revision: a0d554a64d88156834ff5ae9920b964011b16384 + metrics: + - type: cos_sim_pearson + value: 83.40468875905418 + - type: cos_sim_spearman + value: 74.2189990321174 + - type: euclidean_pearson + value: 80.74376966290956 + - type: euclidean_spearman + value: 74.97663839079335 + - type: manhattan_pearson + value: 80.69779331646207 + - type: manhattan_spearman + value: 75.00225252917613 + - task: + type: STS + dataset: + type: mteb/sts13-sts + name: MTEB STS13 + config: default + split: test + revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca + metrics: + - type: cos_sim_pearson + value: 82.5745290053095 + - type: cos_sim_spearman + value: 83.31401180333397 + - type: euclidean_pearson + value: 82.96500607325534 + - type: euclidean_spearman + value: 83.8534967935793 + - type: manhattan_pearson + value: 82.83112050632508 + - type: manhattan_spearman + value: 83.70877296557838 + - task: + type: STS + dataset: + type: mteb/sts14-sts + name: MTEB STS14 + config: default + split: test + revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 + metrics: + - type: cos_sim_pearson + value: 80.67833656607704 + - type: cos_sim_spearman + value: 78.52252410630707 + - type: euclidean_pearson + value: 80.071189514343 + - type: euclidean_spearman + value: 78.95143545742796 + - type: manhattan_pearson + value: 80.0128926165121 + - type: manhattan_spearman + value: 78.91236678732628 + - task: + type: STS + dataset: + type: mteb/sts15-sts + name: MTEB STS15 + config: default + split: test + revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 + metrics: + - type: cos_sim_pearson + value: 87.48437639980746 + - type: cos_sim_spearman + value: 88.34876527774259 + - type: euclidean_pearson + value: 87.64898081823888 + - type: euclidean_spearman + value: 88.58937180804213 + - type: manhattan_pearson + value: 87.5942417815288 + - type: manhattan_spearman + value: 88.53013922267687 + - task: + type: STS + dataset: + type: mteb/sts16-sts + name: MTEB STS16 + config: default + split: test + revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 + metrics: + - type: cos_sim_pearson + value: 82.69189187164781 + - type: cos_sim_spearman + value: 84.15327883572112 + - type: euclidean_pearson + value: 83.64202266685898 + - type: euclidean_spearman + value: 84.6219602318862 + - type: manhattan_pearson + value: 83.53256698709998 + - type: manhattan_spearman + value: 84.49260712904946 + - task: + type: STS + dataset: + type: mteb/sts17-crosslingual-sts + name: MTEB STS17 (en-en) + config: en-en + split: test + revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d + metrics: + - type: cos_sim_pearson + value: 87.09508017611589 + - type: cos_sim_spearman + value: 87.23010990417097 + - type: euclidean_pearson + value: 87.62545569077133 + - type: euclidean_spearman + value: 86.71152051711714 + - type: manhattan_pearson + value: 87.5057154278377 + - type: manhattan_spearman + value: 86.60611898281267 + - task: + type: STS + dataset: + type: mteb/sts22-crosslingual-sts + name: MTEB STS22 (en) + config: en + split: test + revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 + metrics: + - type: cos_sim_pearson + value: 61.72129893941176 + - type: cos_sim_spearman + value: 62.87871412069194 + - type: euclidean_pearson + value: 63.21077648290454 + - type: euclidean_spearman + value: 63.03263080805978 + - type: manhattan_pearson + value: 63.20740860135976 + - type: manhattan_spearman + value: 62.89930471802817 + - task: + type: STS + dataset: + type: mteb/stsbenchmark-sts + name: MTEB STSBenchmark + config: default + split: test + revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 + metrics: + - type: cos_sim_pearson + value: 85.039118236799 + - type: cos_sim_spearman + value: 86.18102563389962 + - type: euclidean_pearson + value: 85.62977041471879 + - type: euclidean_spearman + value: 86.02478990544347 + - type: manhattan_pearson + value: 85.60786740521806 + - type: manhattan_spearman + value: 85.99546210442547 + - task: + type: Reranking + dataset: + type: mteb/scidocs-reranking + name: MTEB SciDocsRR + config: default + split: test + revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab + metrics: + - type: map + value: 82.89875069737266 + - type: mrr + value: 95.42621322033087 + - task: + type: Retrieval + dataset: + type: scifact + name: MTEB SciFact + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 58.660999999999994 + - type: map_at_10 + value: 68.738 + - type: map_at_100 + value: 69.33200000000001 + - type: map_at_1000 + value: 69.352 + - type: map_at_3 + value: 66.502 + - type: map_at_5 + value: 67.686 + - type: mrr_at_1 + value: 61.667 + - type: mrr_at_10 + value: 70.003 + - type: mrr_at_100 + value: 70.441 + - type: mrr_at_1000 + value: 70.46 + - type: mrr_at_3 + value: 68.278 + - type: mrr_at_5 + value: 69.194 + - type: ndcg_at_1 + value: 61.667 + - type: ndcg_at_10 + value: 73.083 + - type: ndcg_at_100 + value: 75.56 + - type: ndcg_at_1000 + value: 76.01400000000001 + - type: ndcg_at_3 + value: 69.28699999999999 + - type: ndcg_at_5 + value: 70.85000000000001 + - type: precision_at_1 + value: 61.667 + - type: precision_at_10 + value: 9.6 + - type: precision_at_100 + value: 1.087 + - type: precision_at_1000 + value: 0.11199999999999999 + - type: precision_at_3 + value: 27.111 + - type: precision_at_5 + value: 17.467 + - type: recall_at_1 + value: 58.660999999999994 + - type: recall_at_10 + value: 85.02199999999999 + - type: recall_at_100 + value: 95.933 + - type: recall_at_1000 + value: 99.333 + - type: recall_at_3 + value: 74.506 + - type: recall_at_5 + value: 78.583 + - task: + type: PairClassification + dataset: + type: mteb/sprintduplicatequestions-pairclassification + name: MTEB SprintDuplicateQuestions + config: default + split: test + revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 + metrics: + - type: cos_sim_accuracy + value: 99.8029702970297 + - type: cos_sim_ap + value: 94.87673936635738 + - type: cos_sim_f1 + value: 90.00502260170768 + - type: cos_sim_precision + value: 90.41372351160445 + - type: cos_sim_recall + value: 89.60000000000001 + - type: dot_accuracy + value: 99.57524752475247 + - type: dot_ap + value: 84.81717934496321 + - type: dot_f1 + value: 78.23026646556059 + - type: dot_precision + value: 78.66531850353893 + - type: dot_recall + value: 77.8 + - type: euclidean_accuracy + value: 99.8029702970297 + - type: euclidean_ap + value: 94.74658253135284 + - type: euclidean_f1 + value: 90.08470353761834 + - type: euclidean_precision + value: 89.77159880834161 + - type: euclidean_recall + value: 90.4 + - type: manhattan_accuracy + value: 99.8 + - type: manhattan_ap + value: 94.69224030742787 + - type: manhattan_f1 + value: 89.9502487562189 + - type: manhattan_precision + value: 89.50495049504951 + - type: manhattan_recall + value: 90.4 + - type: max_accuracy + value: 99.8029702970297 + - type: max_ap + value: 94.87673936635738 + - type: max_f1 + value: 90.08470353761834 + - task: + type: Clustering + dataset: + type: mteb/stackexchange-clustering + name: MTEB StackExchangeClustering + config: default + split: test + revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 + metrics: + - type: v_measure + value: 63.906039623153035 + - task: + type: Clustering + dataset: + type: mteb/stackexchange-clustering-p2p + name: MTEB StackExchangeClusteringP2P + config: default + split: test + revision: 815ca46b2622cec33ccafc3735d572c266efdb44 + metrics: + - type: v_measure + value: 32.56053830923281 + - task: + type: Reranking + dataset: + type: mteb/stackoverflowdupquestions-reranking + name: MTEB StackOverflowDupQuestions + config: default + split: test + revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 + metrics: + - type: map + value: 50.15326538775145 + - type: mrr + value: 50.99279295051355 + - task: + type: Summarization + dataset: + type: mteb/summeval + name: MTEB SummEval + config: default + split: test + revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c + metrics: + - type: cos_sim_pearson + value: 31.44030762047337 + - type: cos_sim_spearman + value: 31.00910300264562 + - type: dot_pearson + value: 26.88257194766013 + - type: dot_spearman + value: 27.646202679013577 + - task: + type: Retrieval + dataset: + type: trec-covid + name: MTEB TRECCOVID + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 0.247 + - type: map_at_10 + value: 1.9429999999999998 + - type: map_at_100 + value: 10.82 + - type: map_at_1000 + value: 25.972 + - type: map_at_3 + value: 0.653 + - type: map_at_5 + value: 1.057 + - type: mrr_at_1 + value: 94 + - type: mrr_at_10 + value: 96.333 + - type: mrr_at_100 + value: 96.333 + - type: mrr_at_1000 + value: 96.333 + - type: mrr_at_3 + value: 96.333 + - type: mrr_at_5 + value: 96.333 + - type: ndcg_at_1 + value: 89 + - type: ndcg_at_10 + value: 79.63799999999999 + - type: ndcg_at_100 + value: 57.961 + - type: ndcg_at_1000 + value: 50.733 + - type: ndcg_at_3 + value: 84.224 + - type: ndcg_at_5 + value: 82.528 + - type: precision_at_1 + value: 94 + - type: precision_at_10 + value: 84.2 + - type: precision_at_100 + value: 59.36 + - type: precision_at_1000 + value: 22.738 + - type: precision_at_3 + value: 88 + - type: precision_at_5 + value: 86.8 + - type: recall_at_1 + value: 0.247 + - type: recall_at_10 + value: 2.131 + - type: recall_at_100 + value: 14.035 + - type: recall_at_1000 + value: 47.457 + - type: recall_at_3 + value: 0.6779999999999999 + - type: recall_at_5 + value: 1.124 + - task: + type: Retrieval + dataset: + type: webis-touche2020 + name: MTEB Touche2020 + config: default + split: test + revision: None + metrics: + - type: map_at_1 + value: 2.603 + - type: map_at_10 + value: 11.667 + - type: map_at_100 + value: 16.474 + - type: map_at_1000 + value: 18.074 + - type: map_at_3 + value: 6.03 + - type: map_at_5 + value: 8.067 + - type: mrr_at_1 + value: 34.694 + - type: mrr_at_10 + value: 51.063 + - type: mrr_at_100 + value: 51.908 + - type: mrr_at_1000 + value: 51.908 + - type: mrr_at_3 + value: 47.959 + - type: mrr_at_5 + value: 49.694 + - type: ndcg_at_1 + value: 32.653 + - type: ndcg_at_10 + value: 28.305000000000003 + - type: ndcg_at_100 + value: 35.311 + - type: ndcg_at_1000 + value: 47.644999999999996 + - type: ndcg_at_3 + value: 32.187 + - type: ndcg_at_5 + value: 29.134999999999998 + - type: precision_at_1 + value: 34.694 + - type: precision_at_10 + value: 26.122 + - type: precision_at_100 + value: 6.755 + - type: precision_at_1000 + value: 1.467 + - type: precision_at_3 + value: 34.694 + - type: precision_at_5 + value: 30.203999999999997 + - type: recall_at_1 + value: 2.603 + - type: recall_at_10 + value: 18.716 + - type: recall_at_100 + value: 42.512 + - type: recall_at_1000 + value: 79.32000000000001 + - type: recall_at_3 + value: 7.59 + - type: recall_at_5 + value: 10.949 + - task: + type: Classification + dataset: + type: mteb/toxic_conversations_50k + name: MTEB ToxicConversationsClassification + config: default + split: test + revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c + metrics: + - type: accuracy + value: 74.117 + - type: ap + value: 15.89357321699319 + - type: f1 + value: 57.14385866369257 + - task: + type: Classification + dataset: + type: mteb/tweet_sentiment_extraction + name: MTEB TweetSentimentExtractionClassification + config: default + split: test + revision: d604517c81ca91fe16a244d1248fc021f9ecee7a + metrics: + - type: accuracy + value: 61.38370118845502 + - type: f1 + value: 61.67038693866553 + - task: + type: Clustering + dataset: + type: mteb/twentynewsgroups-clustering + name: MTEB TwentyNewsgroupsClustering + config: default + split: test + revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 + metrics: + - type: v_measure + value: 42.57754941537969 + - task: + type: PairClassification + dataset: + type: mteb/twittersemeval2015-pairclassification + name: MTEB TwitterSemEval2015 + config: default + split: test + revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 + metrics: + - type: cos_sim_accuracy + value: 86.1775049174465 + - type: cos_sim_ap + value: 74.3994879581554 + - type: cos_sim_f1 + value: 69.32903671308551 + - type: cos_sim_precision + value: 61.48193508879363 + - type: cos_sim_recall + value: 79.47229551451187 + - type: dot_accuracy + value: 81.65345413363534 + - type: dot_ap + value: 59.690898346685096 + - type: dot_f1 + value: 57.27622826467499 + - type: dot_precision + value: 51.34965473948525 + - type: dot_recall + value: 64.74934036939314 + - type: euclidean_accuracy + value: 86.04637301066937 + - type: euclidean_ap + value: 74.33009001775268 + - type: euclidean_f1 + value: 69.2458374142997 + - type: euclidean_precision + value: 64.59570580173595 + - type: euclidean_recall + value: 74.6174142480211 + - type: manhattan_accuracy + value: 86.11193896405793 + - type: manhattan_ap + value: 74.2964140130421 + - type: manhattan_f1 + value: 69.11601528788066 + - type: manhattan_precision + value: 64.86924323073363 + - type: manhattan_recall + value: 73.95778364116094 + - type: max_accuracy + value: 86.1775049174465 + - type: max_ap + value: 74.3994879581554 + - type: max_f1 + value: 69.32903671308551 + - task: + type: PairClassification + dataset: + type: mteb/twitterurlcorpus-pairclassification + name: MTEB TwitterURLCorpus + config: default + split: test + revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf + metrics: + - type: cos_sim_accuracy + value: 89.01501921061823 + - type: cos_sim_ap + value: 85.97819287477351 + - type: cos_sim_f1 + value: 78.33882858518875 + - type: cos_sim_precision + value: 75.49446626204926 + - type: cos_sim_recall + value: 81.40591315060055 + - type: dot_accuracy + value: 86.47494857763806 + - type: dot_ap + value: 78.77420360340282 + - type: dot_f1 + value: 73.06433247936238 + - type: dot_precision + value: 67.92140777983595 + - type: dot_recall + value: 79.04989220819218 + - type: euclidean_accuracy + value: 88.7297706368611 + - type: euclidean_ap + value: 85.61550568529317 + - type: euclidean_f1 + value: 77.84805525263539 + - type: euclidean_precision + value: 73.73639994491117 + - type: euclidean_recall + value: 82.44533415460425 + - type: manhattan_accuracy + value: 88.75111576823068 + - type: manhattan_ap + value: 85.58701671476263 + - type: manhattan_f1 + value: 77.70169909067856 + - type: manhattan_precision + value: 73.37666780704755 + - type: manhattan_recall + value: 82.5685247921158 + - type: max_accuracy + value: 89.01501921061823 + - type: max_ap + value: 85.97819287477351 + - type: max_f1 + value: 78.33882858518875 +language: +- en +license: mit +--- + +## E5-base + +**News (May 2023): please switch to [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2), which has better performance and same method of usage.** + +[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf). +Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 + +This model has 12 layers and the embedding size is 768. + +## Usage + +Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. + +```python +import torch.nn.functional as F + +from torch import Tensor +from transformers import AutoTokenizer, AutoModel + + +def average_pool(last_hidden_states: Tensor, + attention_mask: Tensor) -> Tensor: + last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) + return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] + + +# Each input text should start with "query: " or "passage: ". +# For tasks other than retrieval, you can simply use the "query: " prefix. +input_texts = ['query: how much protein should a female eat', + 'query: summit define', + "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", + "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."] + +tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-base') +model = AutoModel.from_pretrained('intfloat/e5-base') + +# Tokenize the input texts +batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') + +outputs = model(**batch_dict) +embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) + +# normalize embeddings +embeddings = F.normalize(embeddings, p=2, dim=1) +scores = (embeddings[:2] @ embeddings[2:].T) * 100 +print(scores.tolist()) +``` + +## Training Details + +Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf). + +## Benchmark Evaluation + +Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results +on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). + +## Support for Sentence Transformers + +Below is an example for usage with sentence_transformers. +```python +from sentence_transformers import SentenceTransformer +model = SentenceTransformer('intfloat/e5-base') +input_texts = [ + 'query: how much protein should a female eat', + 'query: summit define', + "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", + "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." +] +embeddings = model.encode(input_texts, normalize_embeddings=True) +``` + +Package requirements + +`pip install sentence_transformers~=2.2.2` + +Contributors: [michaelfeil](https://huggingface.co/michaelfeil) + +## FAQ + +**1. Do I need to add the prefix "query: " and "passage: " to input texts?** + +Yes, this is how the model is trained, otherwise you will see a performance degradation. + +Here are some rules of thumb: +- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. + +- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval. + +- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering. + +**2. Why are my reproduced results slightly different from reported in the model card?** + +Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. + +**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?** + +This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss. + +For text embedding tasks like text retrieval or semantic similarity, +what matters is the relative order of the scores instead of the absolute values, +so this should not be an issue. + +## Citation + +If you find our paper or models helpful, please consider cite as follows: + +``` +@article{wang2022text, + title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, + author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, + journal={arXiv preprint arXiv:2212.03533}, + year={2022} +} +``` + +## Limitations + +This model only works for English texts. 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mode 100644 index 0000000000000000000000000000000000000000..6aca282f6b86a3f76251c76e049eca8b2816f951 --- /dev/null +++ b/rag_pipeline/.gitignore @@ -0,0 +1,39 @@ +# langchain +.langchain.db +langflow.db + +# api keys - please see chapter 3 or README for instructions +config.py + +# mac +.DS_Store + +# Distribution/Packaging +.env +.venv +env/ +venv/ +wheels/ +*.egg +.ruff_cache +.mypy_cache. +search_engine/faiss_index.db/ + +# IDEs +.idea +.vs/ +.vscode/ +.spyderproject +.spyproject +.ipynb_checkpoints + + +# Byte-compiled / optimized / DLL files / Cache +__pycache__/ +**/*.pyc +.gradio/ +cache/ + +# Logs +logs/ +*.log diff --git a/rag_pipeline/__init__.py b/rag_pipeline/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/rag_pipeline/doc_load.py b/rag_pipeline/doc_load.py new file mode 100644 index 0000000000000000000000000000000000000000..16cfd3617eb084bd38a142c4d08ed15e47c4aee9 --- /dev/null +++ b/rag_pipeline/doc_load.py @@ -0,0 +1,56 @@ + +import logging +import os +import pathlib +import tempfile +from typing import Any + +from langchain_community.document_loaders.epub import UnstructuredEPubLoader +from langchain_community.document_loaders.pdf import PyPDFLoader +from langchain_community.document_loaders.text import TextLoader +from langchain_community.document_loaders.word_document import UnstructuredWordDocumentLoader + +from langchain_core.documents import Document +from streamlit.logger import get_logger + +logging.basicConfig(encoding="utf-8", level=logging.INFO) +Logger = logging.getLogger(__name__) + + +class EpubReader(UnstructuredEPubLoader): + def __init__(self, file_path: str|list[str], **unstructured_kwargs: Any): + super().__init__(file_path, **unstructured_kwargs, mode="elements", strategy = "fast") + +class DocumentLoaderException(Exception): + pass + +class DocumentLoader(object): + """ loads in a document with a supported extension""" + supported_extensions = { + ".pdf": PyPDFLoader, + ".txt": TextLoader, + ".epub": EpubReader, + ".docx": UnstructuredWordDocumentLoader, + ".doc": UnstructuredWordDocumentLoader, + + } + + +def load_document(temp_filepath: str) -> list[Document]: + + """ + The load_document function takes a file path, determines its extension, selects the appropriate loader from the + supported_extension dictionary, and returns a list of document docoument + """ + ext = pathlib.Path(temp_filepath).suffix + loader = DocumentLoader.supported_extensions.get(ext) + if not loader: + raise DocumentLoaderException( + f"Invalid extension type {ext}, cannot load this type of file" + ) + loaded = loader(temp_filepath) + docs = loaded.load() + logging.info(docs) + return docs + + diff --git a/rag_pipeline/doc_retriever.py b/rag_pipeline/doc_retriever.py new file mode 100644 index 0000000000000000000000000000000000000000..c108d69d3c01ae814b8ef7483811c019797628ac --- /dev/null +++ b/rag_pipeline/doc_retriever.py @@ -0,0 +1,71 @@ +import os +import tempfile +from typing import List, Any + +from langchain_core.callbacks import CallbackManagerForRetrieverRun +from langchain_core.documents import Document +from langchain_core.retrievers import BaseRetriever +from langchain_core.vectorstores import InMemoryVectorStore +from langchain_text_splitters import RecursiveCharacterTextSplitter + +import sys +import os + +# Dynamically add the parent directory to sys.path +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.abspath(os.path.join(current_dir, '..')) +if parent_dir not in sys.path: + sys.path.insert(0, parent_dir) + +from rag_pipeline.doc_load import load_document +from rag_pipeline.llms import Embeddings + +#Setting up a vector store for the retriever to use +VECTOR_STORE = InMemoryVectorStore(embedding = Embeddings) + +def split_documents(docs: List[Document])-> list[Document]: + """Split each document""" + text_splitter = RecursiveCharacterTextSplitter( + chunk_size = 1500, chunk_overlap = 200 + ) + return text_splitter.split_documents(docs) + + +class DocumentRetriever(BaseRetriever): + """A Retriever that contains the top k documents that contain the user query. + + -->store_documents splits the documents and adds them to the vector store + -->add_uploaded_docs() processes files uploaded by the user, stores them temporarily, loads them as document + and adds them to the vector store. + -->_get_relevent_documents() returns top k documents related to a given query from the vector store. + """ + documents: List[Document] = [] + k: int = 5 + + def model_post_init(self, ctx: Any)->None: + self.store_documents(self.documents) + + @staticmethod + def store_documents(docs: List[Document])->None: + """Add Documents to the vector Store""" + splits = split_documents(docs) + VECTOR_STORE.add_documents(splits) + + def add_uploaded_docs(self, uploaded_files): + """Add uploaded documents""" + docs=[] + temp_dir = tempfile.TemporaryDirectory() + for file in uploaded_files: + temp_filepath = os.path.join(temp_dir.name, file.name) + with open(temp_filepath, "wb") as f: + f.write(file.getvalue()) + docs.extend(load_document(temp_filepath)) + self.documents.extend(docs) + self.store_documents(docs) + + def _get_relevant_documents(self, query: str, run_manager: CallbackManagerForRetrieverRun)-> List[Document]: + """Sync implementation for retriever""" + if len(self.documents)==0: + return[] + return VECTOR_STORE.similarity_search(query=query, k=self.k) + diff --git a/rag_pipeline/graph_module.py b/rag_pipeline/graph_module.py new file mode 100644 index 0000000000000000000000000000000000000000..5a4b5c38ddfa151357737a46d6f1c793f73d1077 --- /dev/null +++ b/rag_pipeline/graph_module.py @@ -0,0 +1,121 @@ +from typing import Annotated +from langchain_core.documents import Document +from langchain_core.messages import AIMessage +from langchain_core.prompts import ChatPromptTemplate +from langgraph.checkpoint.memory import MemorySaver +from langgraph.constants import END +from langgraph.graph import START, StateGraph, add_messages +from typing_extensions import List, TypedDict + + +import sys +import os + +# Dynamically add the parent directory to sys.path +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.abspath(os.path.join(current_dir, '..')) +if parent_dir not in sys.path: + sys.path.insert(0, parent_dir) + +from rag_pipeline.doc_retriever import DocumentRetriever +from rag_pipeline.llms import chat_model + +system_prompt = ( + "You are a helpful AI assistant. Given a user question " + "and a set of corporate document snippets, your task is to write documentation that addresses the question. " + "If none of the documents are relevant, explicitly state that no relevant documents were found, " + "and then answer the question to the best of your knowledge. " + "\n\nCorporate document snippets:\n{context}" + "Always cite the document snippets used in your response. " + "Do not fabricate information. If unsure, say so." +) + +retriever = DocumentRetriever() +prompt = ChatPromptTemplate.from_messages( + [ + ("system", system_prompt), + ("human", "{question}"), + ] + +) + +class State(TypedDict): + question: str + context: List[Document] + answer: str + issues_report: str + issues_detected: bool + messages: Annotated[List, add_messages] + + +def retrieve(state: State): #Get the relevant documents based on the most recent message + retrieved_docs = retriever.invoke(state["messages"][-1].content) + print(retrieved_docs) + return {"context": retrieved_docs} + +def generate(state: State): + docs_content = "\n\n".join(doc.page_content for doc in state["context"]) + messages = prompt.invoke( + {"question": state["messages"][-1].content, "context": docs_content} + ) + response = chat_model.invoke(messages) + print(response.content) + return {"answer": response.content} + +#Validation of the Genereated content by another LLM - We can also do human in the loop + +def double_check(state: State): + result = chat_model.invoke( + [{ + "role": "user", + "content": ( + f"Review the following project documentation for compliance with our corporate standards" + f"Return 'ISSUES FOUND' followed by any issues detected or 'NO ISSUES': {state['answer']}" + ) + }] + + ) + if 'ISSUES FOUND' in result.content: + print("issues detected") + return { + "issues_report": result.split("ISSUES FOUND", 1)[1].strip(), + "issues_detected": True + } + else: + print("no issues detected") + return { + "issues_report": "", + "issues_detected": False + } + +def doc_finalizer(state: State): + if state.get("issues_detected"): + prompt_text = ( + f"Revise the following documentation to address these feedback points:\n{state['issues_report']}\n\n" + f"Original Document:\n{state['answer']}\n\n" + "Always return the full revised document, even if no changes are needed." + ) + response = chat_model.invoke([{"role": "user", "content": prompt_text}]) + return { + "messages": [AIMessage(response.content)] + } + else: + return { + "messages": [AIMessage(state["answer"])] + } + +#Contruct the Graph + +graph_builder = StateGraph(State).add_sequence( + [retrieve, generate, double_check, doc_finalizer] +) + +graph_builder.add_edge(START, "retrieve") +graph_builder.add_edge("doc_finalizer", END) +memory = MemorySaver() +graph = graph_builder.compile(checkpointer=memory) #- Compiles the graph into a runnable object. +#- checkpointer=memory enables state persistence across runs. +config = {"configurable": {"thread_id": "abc123"}} +#Sets runtime configuration, possibly for threading, logging or session tracking + + diff --git a/rag_pipeline/llms.py b/rag_pipeline/llms.py new file mode 100644 index 0000000000000000000000000000000000000000..4f735540ef65918341664f3f3bd1bd8b1977b5af --- /dev/null +++ b/rag_pipeline/llms.py @@ -0,0 +1,45 @@ +#LLLM.py + +from langchain.embeddings import CacheBackedEmbeddings +from langchain.storage import LocalFileStore +from langchain_groq import ChatGroq +from langchain_openai import OpenAIEmbeddings +from langchain_community.embeddings import HuggingFaceEmbeddings + + +import sys +import os + +# Dynamically add the parent directory to sys.path +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.abspath(os.path.join(current_dir, '..')) +if parent_dir not in sys.path: + sys.path.insert(0, parent_dir) + +from config import set_environment + +chat_model = ChatGroq( + model = "deepseek-r1-distill-llama-70b", + #model = "llama-3.3-70b-versatile", + temperature = 0, + max_tokens = 1024, + timeout = None, + max_retries = 2, +) + + +# Step 1: Set up local byte store for caching +store = LocalFileStore("./cache/") + +# Step 2: Initialize HuggingFace embeddings +underlying_embeddings = HuggingFaceEmbeddings( + model_name="intfloat/e5-base", # High-quality embedding model + cache_folder="./embeddings_cache" # Optional: local cache for model files +) + +# Step 3: Wrap with cache-backed embeddings +Embeddings = CacheBackedEmbeddings.from_bytes_store( + underlying_embeddings, + store, + namespace="intfloat/e5-base" # Explicit namespace for cache isolation +) diff --git a/rag_pipeline/rag_streamlit_app.py b/rag_pipeline/rag_streamlit_app.py new file mode 100644 index 0000000000000000000000000000000000000000..753e8654ece9fb30f1e327d2bb8306918c6f0511 --- /dev/null +++ b/rag_pipeline/rag_streamlit_app.py @@ -0,0 +1,85 @@ +import streamlit as st +from langchain_core.messages import HumanMessage +from doc_load import DocumentLoader +import sys, os + +# Path setup +current_dir = os.path.dirname(os.path.abspath(__file__)) +parent_dir = os.path.abspath(os.path.join(current_dir, '..')) +if parent_dir not in sys.path: + sys.path.insert(0, parent_dir) + +from config import set_environment +set_environment() + +from rag_pipeline.graph_module import graph, config, retriever + +# Page config +st.set_page_config(page_title="📁 Corporate Documentation Manager", layout="wide") + +# Session state init +if "chat_history" not in st.session_state: + st.session_state.chat_history = [] +if "uploaded_files" not in st.session_state: + st.session_state.uploaded_files = [] + +# Header +st.markdown(""" +
Chat with your documents and get contextual answers with citations
+