From 3d656e81d9487ae8e5f80156083e6d497db8df12 Mon Sep 17 00:00:00 2001
From: xxl <505279206@qq.com>
Date: Mon, 6 Jan 2025 09:41:49 +0800
Subject: [PATCH] first commit
---
1_Pooling/config.json | 10 +
README.md | 3157 +-
config.json | 46 +
config_sentence_transformers.json | 10 +
model.safetensors | 3 +
modernbertembed.png | 3 +
modules.json | 20 +
onnx/model.onnx | 3 +
onnx/model_bnb4.onnx | 3 +
onnx/model_fp16.onnx | 3 +
onnx/model_int8.onnx | 3 +
onnx/model_q4.onnx | 3 +
onnx/model_q4f16.onnx | 3 +
onnx/model_quantized.onnx | 3 +
onnx/model_uint8.onnx | 3 +
sentence_bert_config.json | 4 +
special_tokens_map.json | 37 +
tokenizer.json | 251498 +++++++++++++++++++++++++++
tokenizer_config.json | 945 +
19 files changed, 255755 insertions(+), 2 deletions(-)
create mode 100644 1_Pooling/config.json
create mode 100644 config.json
create mode 100644 config_sentence_transformers.json
create mode 100644 model.safetensors
create mode 100644 modernbertembed.png
create mode 100644 modules.json
create mode 100644 onnx/model.onnx
create mode 100644 onnx/model_bnb4.onnx
create mode 100644 onnx/model_fp16.onnx
create mode 100644 onnx/model_int8.onnx
create mode 100644 onnx/model_q4.onnx
create mode 100644 onnx/model_q4f16.onnx
create mode 100644 onnx/model_quantized.onnx
create mode 100644 onnx/model_uint8.onnx
create mode 100644 sentence_bert_config.json
create mode 100644 special_tokens_map.json
create mode 100644 tokenizer.json
create mode 100644 tokenizer_config.json
diff --git a/1_Pooling/config.json b/1_Pooling/config.json
new file mode 100644
index 0000000..9213783
--- /dev/null
+++ b/1_Pooling/config.json
@@ -0,0 +1,10 @@
+{
+ "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,
+ "pooling_mode_weightedmean_tokens": false,
+ "pooling_mode_lasttoken": false,
+ "include_prompt": true
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index d7937d0..444fa74 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,3156 @@
-# modernbert-embed-base_a14190476129333248528824
+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- mteb
+- transformers.js
+model-index:
+- name: binarize_False
+ results:
+ - task:
+ type: Classification
+ dataset:
+ type: None
+ name: MTEB AmazonCounterfactualClassification (en)
+ config: en
+ split: test
+ revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+ metrics:
+ - type: accuracy
+ value: 78.13432835820896
+ - type: ap
+ value: 42.190424731303246
+ - type: f1
+ value: 72.34446401534811
+ - task:
+ type: Classification
+ dataset:
+ type: None
+ name: MTEB AmazonPolarityClassification
+ config: default
+ split: test
+ revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+ metrics:
+ - type: accuracy
+ value: 93.093825
+ - type: ap
+ value: 90.03727505544286
+ - type: f1
+ value: 93.0874055138833
+ - task:
+ type: Classification
+ dataset:
+ type: None
+ name: MTEB AmazonReviewsClassification (en)
+ config: en
+ split: test
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+ metrics:
+ - type: accuracy
+ value: 48.428000000000004
+ - type: f1
+ value: 47.74311520203536
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB ArguAna
+ config: default
+ split: test
+ revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
+ metrics:
+ - type: map_at_1
+ value: 23.898
+ - type: map_at_10
+ value: 39.775
+ - type: map_at_100
+ value: 40.827000000000005
+ - type: map_at_1000
+ value: 40.837
+ - type: map_at_20
+ value: 40.604
+ - type: map_at_3
+ value: 34.519
+ - type: map_at_5
+ value: 37.307
+ - type: mrr_at_1
+ value: 24.395
+ - type: mrr_at_10
+ value: 39.963
+ - type: mrr_at_100
+ value: 41.014
+ - type: mrr_at_1000
+ value: 41.024
+ - type: mrr_at_20
+ value: 40.791
+ - type: mrr_at_3
+ value: 34.732
+ - type: mrr_at_5
+ value: 37.480999999999995
+ - type: ndcg_at_1
+ value: 23.898
+ - type: ndcg_at_10
+ value: 48.962
+ - type: ndcg_at_100
+ value: 53.386
+ - type: ndcg_at_1000
+ value: 53.634
+ - type: ndcg_at_20
+ value: 51.898999999999994
+ - type: ndcg_at_3
+ value: 38.034
+ - type: ndcg_at_5
+ value: 43.036
+ - type: precision_at_1
+ value: 23.898
+ - type: precision_at_10
+ value: 7.852
+ - type: precision_at_100
+ value: 0.9769999999999999
+ - type: precision_at_1000
+ value: 0.1
+ - type: precision_at_20
+ value: 4.4990000000000006
+ - type: precision_at_3
+ value: 16.073999999999998
+ - type: precision_at_5
+ value: 12.063
+ - type: recall_at_1
+ value: 23.898
+ - type: recall_at_10
+ value: 78.521
+ - type: recall_at_100
+ value: 97.724
+ - type: recall_at_1000
+ value: 99.644
+ - type: recall_at_20
+ value: 89.972
+ - type: recall_at_3
+ value: 48.222
+ - type: recall_at_5
+ value: 60.313
+ - task:
+ type: Clustering
+ dataset:
+ type: None
+ name: MTEB ArxivClusteringP2P
+ config: default
+ split: test
+ revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+ metrics:
+ - type: v_measure
+ value: 47.69067314293749
+ - type: v_measures
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+ - task:
+ type: Clustering
+ dataset:
+ type: None
+ name: MTEB ArxivClusteringS2S
+ config: default
+ split: test
+ revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+ metrics:
+ - type: v_measure
+ value: 38.0916537995626
+ - type: v_measures
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+ - task:
+ type: Reranking
+ dataset:
+ type: None
+ name: MTEB AskUbuntuDupQuestions
+ config: default
+ split: test
+ revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+ metrics:
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+ value: 62.33195643912506
+ - type: mrr
+ value: 76.43978366970057
+ - task:
+ type: STS
+ dataset:
+ type: None
+ name: MTEB BIOSSES
+ config: default
+ split: test
+ revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+ metrics:
+ - type: cos_sim_pearson
+ value: 81.20285894915236
+ - type: cos_sim_spearman
+ value: 78.16322678527897
+ - type: euclidean_pearson
+ value: 80.6118408638417
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+ value: 78.19033583671204
+ - type: manhattan_pearson
+ value: 80.41282660275819
+ - type: manhattan_spearman
+ value: 77.98611431591628
+ - task:
+ type: Classification
+ dataset:
+ type: None
+ name: MTEB Banking77Classification
+ config: default
+ split: test
+ revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+ metrics:
+ - type: accuracy
+ value: 85.25324675324676
+ - type: f1
+ value: 85.19854235582687
+ - task:
+ type: Clustering
+ dataset:
+ type: None
+ name: MTEB BiorxivClusteringP2P
+ config: default
+ split: test
+ revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+ metrics:
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+ value: 39.65216461057432
+ - type: v_measures
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+ - task:
+ type: Clustering
+ dataset:
+ type: None
+ name: MTEB BiorxivClusteringS2S
+ config: default
+ split: test
+ revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+ metrics:
+ - type: v_measure
+ value: 33.28787287895752
+ - type: v_measures
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+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackAndroidRetrieval
+ config: default
+ split: test
+ revision: f46a197baaae43b4f621051089b82a364682dfeb
+ metrics:
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+ - type: precision_at_1000
+ value: 0.19
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+ - type: precision_at_3
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+ - type: precision_at_5
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+ - type: recall_at_1
+ value: 32.677
+ - type: recall_at_10
+ value: 61.71
+ - type: recall_at_100
+ value: 82.431
+ - type: recall_at_1000
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+ - type: recall_at_20
+ value: 68.73700000000001
+ - type: recall_at_3
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+ - type: recall_at_5
+ value: 53.739000000000004
+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackEnglishRetrieval
+ config: default
+ split: test
+ revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+ metrics:
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+ - type: recall_at_1
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+ - type: recall_at_3
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+ - task:
+ type: Retrieval
+ dataset:
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+ name: MTEB CQADupstackGamingRetrieval
+ config: default
+ split: test
+ revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+ metrics:
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+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackGisRetrieval
+ config: default
+ split: test
+ revision: 5003b3064772da1887988e05400cf3806fe491f2
+ metrics:
+ - type: map_at_1
+ value: 28.648
+ - type: map_at_10
+ value: 36.388999999999996
+ - type: map_at_100
+ value: 37.372
+ - type: map_at_1000
+ value: 37.457
+ - type: map_at_20
+ value: 36.912
+ - type: map_at_3
+ value: 34.076
+ - type: map_at_5
+ value: 35.415
+ - type: mrr_at_1
+ value: 30.508000000000003
+ - type: mrr_at_10
+ value: 38.132
+ - type: mrr_at_100
+ value: 39.04
+ - type: mrr_at_1000
+ value: 39.106
+ - type: mrr_at_20
+ value: 38.643
+ - type: mrr_at_3
+ value: 35.876000000000005
+ - type: mrr_at_5
+ value: 37.208999999999996
+ - type: ndcg_at_1
+ value: 30.508000000000003
+ - type: ndcg_at_10
+ value: 40.762
+ - type: ndcg_at_100
+ value: 45.732
+ - type: ndcg_at_1000
+ value: 47.799
+ - type: ndcg_at_20
+ value: 42.591
+ - type: ndcg_at_3
+ value: 36.266999999999996
+ - type: ndcg_at_5
+ value: 38.58
+ - type: precision_at_1
+ value: 30.508000000000003
+ - type: precision_at_10
+ value: 6.010999999999999
+ - type: precision_at_100
+ value: 0.897
+ - type: precision_at_1000
+ value: 0.11100000000000002
+ - type: precision_at_20
+ value: 3.412
+ - type: precision_at_3
+ value: 14.991
+ - type: precision_at_5
+ value: 10.328
+ - type: recall_at_1
+ value: 28.648
+ - type: recall_at_10
+ value: 52.342999999999996
+ - type: recall_at_100
+ value: 75.268
+ - type: recall_at_1000
+ value: 90.641
+ - type: recall_at_20
+ value: 59.303
+ - type: recall_at_3
+ value: 40.447
+ - type: recall_at_5
+ value: 46.117000000000004
+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackMathematicaRetrieval
+ config: default
+ split: test
+ revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+ metrics:
+ - type: map_at_1
+ value: 18.476
+ - type: map_at_10
+ value: 27.148
+ - type: map_at_100
+ value: 28.317999999999998
+ - type: map_at_1000
+ value: 28.427999999999997
+ - type: map_at_20
+ value: 27.764
+ - type: map_at_3
+ value: 24.801000000000002
+ - type: map_at_5
+ value: 26.133
+ - type: mrr_at_1
+ value: 22.886
+ - type: mrr_at_10
+ value: 31.741000000000003
+ - type: mrr_at_100
+ value: 32.708
+ - type: mrr_at_1000
+ value: 32.769
+ - type: mrr_at_20
+ value: 32.296
+ - type: mrr_at_3
+ value: 29.498
+ - type: mrr_at_5
+ value: 30.773
+ - type: ndcg_at_1
+ value: 22.886
+ - type: ndcg_at_10
+ value: 32.265
+ - type: ndcg_at_100
+ value: 37.829
+ - type: ndcg_at_1000
+ value: 40.558
+ - type: ndcg_at_20
+ value: 34.372
+ - type: ndcg_at_3
+ value: 28.105000000000004
+ - type: ndcg_at_5
+ value: 30.04
+ - type: precision_at_1
+ value: 22.886
+ - type: precision_at_10
+ value: 5.808
+ - type: precision_at_100
+ value: 0.985
+ - type: precision_at_1000
+ value: 0.13699999999999998
+ - type: precision_at_20
+ value: 3.495
+ - type: precision_at_3
+ value: 13.639999999999999
+ - type: precision_at_5
+ value: 9.577
+ - type: recall_at_1
+ value: 18.476
+ - type: recall_at_10
+ value: 43.442
+ - type: recall_at_100
+ value: 67.376
+ - type: recall_at_1000
+ value: 86.874
+ - type: recall_at_20
+ value: 51.038
+ - type: recall_at_3
+ value: 31.785999999999998
+ - type: recall_at_5
+ value: 36.858999999999995
+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackPhysicsRetrieval
+ config: default
+ split: test
+ revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+ metrics:
+ - type: map_at_1
+ value: 29.098000000000003
+ - type: map_at_10
+ value: 38.97
+ - type: map_at_100
+ value: 40.293
+ - type: map_at_1000
+ value: 40.397
+ - type: map_at_20
+ value: 39.778999999999996
+ - type: map_at_3
+ value: 35.723
+ - type: map_at_5
+ value: 37.519999999999996
+ - type: mrr_at_1
+ value: 35.515
+ - type: mrr_at_10
+ value: 44.55
+ - type: mrr_at_100
+ value: 45.37
+ - type: mrr_at_1000
+ value: 45.412
+ - type: mrr_at_20
+ value: 45.054
+ - type: mrr_at_3
+ value: 41.835
+ - type: mrr_at_5
+ value: 43.356
+ - type: ndcg_at_1
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+ - type: ndcg_at_10
+ value: 44.91
+ - type: ndcg_at_100
+ value: 50.27700000000001
+ - type: ndcg_at_1000
+ value: 52.215
+ - type: ndcg_at_20
+ value: 47.235
+ - type: ndcg_at_3
+ value: 39.505
+ - type: ndcg_at_5
+ value: 42.016
+ - type: precision_at_1
+ value: 35.515
+ - type: precision_at_10
+ value: 8.152
+ - type: precision_at_100
+ value: 1.262
+ - type: precision_at_1000
+ value: 0.16
+ - type: precision_at_20
+ value: 4.851
+ - type: precision_at_3
+ value: 18.447
+ - type: precision_at_5
+ value: 13.321
+ - type: recall_at_1
+ value: 29.098000000000003
+ - type: recall_at_10
+ value: 57.115
+ - type: recall_at_100
+ value: 79.467
+ - type: recall_at_1000
+ value: 92.162
+ - type: recall_at_20
+ value: 65.161
+ - type: recall_at_3
+ value: 42.254000000000005
+ - type: recall_at_5
+ value: 48.415
+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackProgrammersRetrieval
+ config: default
+ split: test
+ revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+ metrics:
+ - type: map_at_1
+ value: 27.372000000000003
+ - type: map_at_10
+ value: 37.781
+ - type: map_at_100
+ value: 39.128
+ - type: map_at_1000
+ value: 39.238
+ - type: map_at_20
+ value: 38.592
+ - type: map_at_3
+ value: 34.782999999999994
+ - type: map_at_5
+ value: 36.466
+ - type: mrr_at_1
+ value: 33.904
+ - type: mrr_at_10
+ value: 43.15
+ - type: mrr_at_100
+ value: 44.049
+ - type: mrr_at_1000
+ value: 44.107
+ - type: mrr_at_20
+ value: 43.721
+ - type: mrr_at_3
+ value: 40.677
+ - type: mrr_at_5
+ value: 42.19
+ - type: ndcg_at_1
+ value: 33.904
+ - type: ndcg_at_10
+ value: 43.527
+ - type: ndcg_at_100
+ value: 49.004999999999995
+ - type: ndcg_at_1000
+ value: 51.276999999999994
+ - type: ndcg_at_20
+ value: 45.988
+ - type: ndcg_at_3
+ value: 38.824999999999996
+ - type: ndcg_at_5
+ value: 41.04
+ - type: precision_at_1
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+ - type: precision_at_10
+ value: 7.854
+ - type: precision_at_100
+ value: 1.2309999999999999
+ - type: precision_at_1000
+ value: 0.16
+ - type: precision_at_20
+ value: 4.692
+ - type: precision_at_3
+ value: 18.531
+ - type: precision_at_5
+ value: 13.150999999999998
+ - type: recall_at_1
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+ - type: recall_at_10
+ value: 55.245999999999995
+ - type: recall_at_100
+ value: 78.278
+ - type: recall_at_1000
+ value: 93.718
+ - type: recall_at_20
+ value: 64.095
+ - type: recall_at_3
+ value: 41.665
+ - type: recall_at_5
+ value: 47.632000000000005
+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackRetrieval
+ config: default
+ split: test
+ revision: f46a197baaae43b4f621051089b82a364682dfeb
+ metrics:
+ - type: map_at_1
+ value: 27.734166666666667
+ - type: map_at_10
+ value: 36.858
+ - type: map_at_100
+ value: 38.043833333333325
+ - type: map_at_1000
+ value: 38.15541666666667
+ - type: map_at_20
+ value: 37.521249999999995
+ - type: map_at_3
+ value: 34.07658333333333
+ - type: map_at_5
+ value: 35.62683333333333
+ - type: mrr_at_1
+ value: 32.676249999999996
+ - type: mrr_at_10
+ value: 40.999
+ - type: mrr_at_100
+ value: 41.835
+ - type: mrr_at_1000
+ value: 41.8895
+ - type: mrr_at_20
+ value: 41.4865
+ - type: mrr_at_3
+ value: 38.645
+ - type: mrr_at_5
+ value: 39.99725000000001
+ - type: ndcg_at_1
+ value: 32.676249999999996
+ - type: ndcg_at_10
+ value: 42.08016666666666
+ - type: ndcg_at_100
+ value: 47.082750000000004
+ - type: ndcg_at_1000
+ value: 49.276583333333335
+ - type: ndcg_at_20
+ value: 44.04808333333334
+ - type: ndcg_at_3
+ value: 37.43375
+ - type: ndcg_at_5
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+ - type: precision_at_1
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+ - type: precision_at_10
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+ - type: precision_at_100
+ value: 1.1458333333333333
+ - type: precision_at_1000
+ value: 0.152
+ - type: precision_at_20
+ value: 4.282916666666667
+ - type: precision_at_3
+ value: 17.061416666666666
+ - type: precision_at_5
+ value: 12.05466666666667
+ - type: recall_at_1
+ value: 27.734166666666667
+ - type: recall_at_10
+ value: 53.33574999999999
+ - type: recall_at_100
+ value: 75.16275
+ - type: recall_at_1000
+ value: 90.34891666666665
+ - type: recall_at_20
+ value: 60.4935
+ - type: recall_at_3
+ value: 40.377916666666664
+ - type: recall_at_5
+ value: 46.0195
+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackStatsRetrieval
+ config: default
+ split: test
+ revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+ metrics:
+ - type: map_at_1
+ value: 25.653
+ - type: map_at_10
+ value: 32.151
+ - type: map_at_100
+ value: 33.152
+ - type: map_at_1000
+ value: 33.243
+ - type: map_at_20
+ value: 32.717
+ - type: map_at_3
+ value: 30.287
+ - type: map_at_5
+ value: 31.25
+ - type: mrr_at_1
+ value: 28.988000000000003
+ - type: mrr_at_10
+ value: 35.131
+ - type: mrr_at_100
+ value: 36.002
+ - type: mrr_at_1000
+ value: 36.069
+ - type: mrr_at_20
+ value: 35.61
+ - type: mrr_at_3
+ value: 33.308
+ - type: mrr_at_5
+ value: 34.259
+ - type: ndcg_at_1
+ value: 28.988000000000003
+ - type: ndcg_at_10
+ value: 35.988
+ - type: ndcg_at_100
+ value: 40.764
+ - type: ndcg_at_1000
+ value: 43.112
+ - type: ndcg_at_20
+ value: 37.852999999999994
+ - type: ndcg_at_3
+ value: 32.562000000000005
+ - type: ndcg_at_5
+ value: 33.983000000000004
+ - type: precision_at_1
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+ - type: precision_at_10
+ value: 5.475
+ - type: precision_at_100
+ value: 0.8500000000000001
+ - type: precision_at_1000
+ value: 0.11199999999999999
+ - type: precision_at_20
+ value: 3.229
+ - type: precision_at_3
+ value: 13.905999999999999
+ - type: precision_at_5
+ value: 9.386999999999999
+ - type: recall_at_1
+ value: 25.653
+ - type: recall_at_10
+ value: 44.962
+ - type: recall_at_100
+ value: 66.405
+ - type: recall_at_1000
+ value: 83.88799999999999
+ - type: recall_at_20
+ value: 51.79899999999999
+ - type: recall_at_3
+ value: 35.144999999999996
+ - type: recall_at_5
+ value: 38.814
+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackTexRetrieval
+ config: default
+ split: test
+ revision: 46989137a86843e03a6195de44b09deda022eec7
+ metrics:
+ - type: map_at_1
+ value: 17.825
+ - type: map_at_10
+ value: 25.592
+ - type: map_at_100
+ value: 26.613999999999997
+ - type: map_at_1000
+ value: 26.734
+ - type: map_at_20
+ value: 26.115
+ - type: map_at_3
+ value: 23.119
+ - type: map_at_5
+ value: 24.54
+ - type: mrr_at_1
+ value: 21.335
+ - type: mrr_at_10
+ value: 29.165000000000003
+ - type: mrr_at_100
+ value: 30.049
+ - type: mrr_at_1000
+ value: 30.121
+ - type: mrr_at_20
+ value: 29.639
+ - type: mrr_at_3
+ value: 26.863999999999997
+ - type: mrr_at_5
+ value: 28.185
+ - type: ndcg_at_1
+ value: 21.335
+ - type: ndcg_at_10
+ value: 30.357
+ - type: ndcg_at_100
+ value: 35.410000000000004
+ - type: ndcg_at_1000
+ value: 38.24
+ - type: ndcg_at_20
+ value: 32.08
+ - type: ndcg_at_3
+ value: 25.95
+ - type: ndcg_at_5
+ value: 28.081
+ - type: precision_at_1
+ value: 21.335
+ - type: precision_at_10
+ value: 5.506
+ - type: precision_at_100
+ value: 0.928
+ - type: precision_at_1000
+ value: 0.135
+ - type: precision_at_20
+ value: 3.2550000000000003
+ - type: precision_at_3
+ value: 12.239
+ - type: precision_at_5
+ value: 8.885
+ - type: recall_at_1
+ value: 17.825
+ - type: recall_at_10
+ value: 41.105999999999995
+ - type: recall_at_100
+ value: 64.17
+ - type: recall_at_1000
+ value: 84.19200000000001
+ - type: recall_at_20
+ value: 47.497
+ - type: recall_at_3
+ value: 28.862
+ - type: recall_at_5
+ value: 34.348
+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackUnixRetrieval
+ config: default
+ split: test
+ revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+ metrics:
+ - type: map_at_1
+ value: 29.435
+ - type: map_at_10
+ value: 38.261
+ - type: map_at_100
+ value: 39.242
+ - type: map_at_1000
+ value: 39.347
+ - type: map_at_20
+ value: 38.742
+ - type: map_at_3
+ value: 35.457
+ - type: map_at_5
+ value: 37.043
+ - type: mrr_at_1
+ value: 34.235
+ - type: mrr_at_10
+ value: 42.24
+ - type: mrr_at_100
+ value: 42.988
+ - type: mrr_at_1000
+ value: 43.043
+ - type: mrr_at_20
+ value: 42.613
+ - type: mrr_at_3
+ value: 39.832
+ - type: mrr_at_5
+ value: 41.227000000000004
+ - type: ndcg_at_1
+ value: 34.235
+ - type: ndcg_at_10
+ value: 43.384
+ - type: ndcg_at_100
+ value: 48.14
+ - type: ndcg_at_1000
+ value: 50.414
+ - type: ndcg_at_20
+ value: 44.913
+ - type: ndcg_at_3
+ value: 38.454
+ - type: ndcg_at_5
+ value: 40.776
+ - type: precision_at_1
+ value: 34.235
+ - type: precision_at_10
+ value: 7.164
+ - type: precision_at_100
+ value: 1.065
+ - type: precision_at_1000
+ value: 0.13699999999999998
+ - type: precision_at_20
+ value: 4.021
+ - type: precision_at_3
+ value: 17.226
+ - type: precision_at_5
+ value: 12.071
+ - type: recall_at_1
+ value: 29.435
+ - type: recall_at_10
+ value: 54.93900000000001
+ - type: recall_at_100
+ value: 76.176
+ - type: recall_at_1000
+ value: 91.989
+ - type: recall_at_20
+ value: 60.451
+ - type: recall_at_3
+ value: 41.332
+ - type: recall_at_5
+ value: 47.316
+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackWebmastersRetrieval
+ config: default
+ split: test
+ revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+ metrics:
+ - type: map_at_1
+ value: 25.605
+ - type: map_at_10
+ value: 34.162
+ - type: map_at_100
+ value: 35.827999999999996
+ - type: map_at_1000
+ value: 36.04
+ - type: map_at_20
+ value: 35.016000000000005
+ - type: map_at_3
+ value: 30.984
+ - type: map_at_5
+ value: 32.717
+ - type: mrr_at_1
+ value: 30.435000000000002
+ - type: mrr_at_10
+ value: 38.681
+ - type: mrr_at_100
+ value: 39.656000000000006
+ - type: mrr_at_1000
+ value: 39.71
+ - type: mrr_at_20
+ value: 39.208999999999996
+ - type: mrr_at_3
+ value: 35.903
+ - type: mrr_at_5
+ value: 37.454
+ - type: ndcg_at_1
+ value: 30.435000000000002
+ - type: ndcg_at_10
+ value: 39.916000000000004
+ - type: ndcg_at_100
+ value: 45.958
+ - type: ndcg_at_1000
+ value: 48.449999999999996
+ - type: ndcg_at_20
+ value: 42.085
+ - type: ndcg_at_3
+ value: 34.696
+ - type: ndcg_at_5
+ value: 37.147000000000006
+ - type: precision_at_1
+ value: 30.435000000000002
+ - type: precision_at_10
+ value: 7.767
+ - type: precision_at_100
+ value: 1.547
+ - type: precision_at_1000
+ value: 0.23800000000000002
+ - type: precision_at_20
+ value: 4.941
+ - type: precision_at_3
+ value: 16.073999999999998
+ - type: precision_at_5
+ value: 11.937000000000001
+ - type: recall_at_1
+ value: 25.605
+ - type: recall_at_10
+ value: 50.654999999999994
+ - type: recall_at_100
+ value: 77.609
+ - type: recall_at_1000
+ value: 93.518
+ - type: recall_at_20
+ value: 58.845000000000006
+ - type: recall_at_3
+ value: 36.272
+ - type: recall_at_5
+ value: 42.596000000000004
+ - task:
+ type: Retrieval
+ dataset:
+ type: BeIR/cqadupstack
+ name: MTEB CQADupstackWordpressRetrieval
+ config: default
+ split: test
+ revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+ metrics:
+ - type: map_at_1
+ value: 23.666
+ - type: map_at_10
+ value: 30.980999999999998
+ - type: map_at_100
+ value: 32.0
+ - type: map_at_1000
+ value: 32.098
+ - type: map_at_20
+ value: 31.621
+ - type: map_at_3
+ value: 28.449999999999996
+ - type: map_at_5
+ value: 29.731999999999996
+ - type: mrr_at_1
+ value: 25.692999999999998
+ - type: mrr_at_10
+ value: 32.788000000000004
+ - type: mrr_at_100
+ value: 33.783
+ - type: mrr_at_1000
+ value: 33.849000000000004
+ - type: mrr_at_20
+ value: 33.408
+ - type: mrr_at_3
+ value: 30.561
+ - type: mrr_at_5
+ value: 31.716
+ - type: ndcg_at_1
+ value: 25.692999999999998
+ - type: ndcg_at_10
+ value: 35.428
+ - type: ndcg_at_100
+ value: 40.375
+ - type: ndcg_at_1000
+ value: 42.802
+ - type: ndcg_at_20
+ value: 37.621
+ - type: ndcg_at_3
+ value: 30.476999999999997
+ - type: ndcg_at_5
+ value: 32.621
+ - type: precision_at_1
+ value: 25.692999999999998
+ - type: precision_at_10
+ value: 5.508
+ - type: precision_at_100
+ value: 0.848
+ - type: precision_at_1000
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+ - type: precision_at_20
+ value: 3.272
+ - type: precision_at_3
+ value: 12.631
+ - type: precision_at_5
+ value: 8.872
+ - type: recall_at_1
+ value: 23.666
+ - type: recall_at_10
+ value: 47.532000000000004
+ - type: recall_at_100
+ value: 69.73700000000001
+ - type: recall_at_1000
+ value: 87.83800000000001
+ - type: recall_at_20
+ value: 55.61000000000001
+ - type: recall_at_3
+ value: 34.06
+ - type: recall_at_5
+ value: 39.254
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB ClimateFEVER
+ config: default
+ split: test
+ revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: mrr_at_100
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+ - type: mrr_at_1000
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+ - type: recall_at_10
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+ - type: recall_at_100
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+ - type: recall_at_1000
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+ - type: recall_at_20
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+ - type: recall_at_3
+ value: 27.279999999999998
+ - type: recall_at_5
+ value: 32.621
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB DBPedia
+ config: default
+ split: test
+ revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_1000
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: mrr_at_100
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+ - type: mrr_at_1000
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+ - type: mrr_at_20
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+ - type: mrr_at_3
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+ - type: mrr_at_5
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+ - type: ndcg_at_1
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+ - type: ndcg_at_10
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+ - type: ndcg_at_1000
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+ - type: ndcg_at_3
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+ - type: precision_at_100
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+ - type: precision_at_1000
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+ - type: precision_at_3
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+ - type: precision_at_5
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+ - type: recall_at_1
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+ - type: recall_at_10
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+ - type: recall_at_1000
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - task:
+ type: Classification
+ dataset:
+ type: None
+ name: MTEB EmotionClassification
+ config: default
+ split: test
+ revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+ metrics:
+ - type: accuracy
+ value: 48.259999999999984
+ - type: f1
+ value: 43.1498589523159
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB FEVER
+ config: default
+ split: test
+ revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
+ metrics:
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+ - type: map_at_10
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+ - type: map_at_1000
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+ - type: ndcg_at_1000
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+ - type: recall_at_20
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+ - type: recall_at_3
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+ - type: recall_at_5
+ value: 92.965
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB FiQA2018
+ config: default
+ split: test
+ revision: 27a168819829fe9bcd655c2df245fb19452e8e06
+ metrics:
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+ - type: map_at_10
+ value: 32.731
+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: mrr_at_100
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+ - type: mrr_at_1000
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+ - type: mrr_at_20
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+ - type: mrr_at_3
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+ - type: mrr_at_5
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+ - type: ndcg_at_1
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+ - type: ndcg_at_10
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+ - type: ndcg_at_100
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+ - type: ndcg_at_1000
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+ - type: ndcg_at_20
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+ - type: ndcg_at_3
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+ - type: ndcg_at_5
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+ - type: precision_at_100
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+ - type: precision_at_1000
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+ - type: precision_at_20
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+ - type: precision_at_5
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+ - type: recall_at_1
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+ - type: recall_at_10
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+ - type: recall_at_100
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+ - type: recall_at_1000
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+ - type: recall_at_20
+ value: 55.706999999999994
+ - type: recall_at_3
+ value: 33.346
+ - type: recall_at_5
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+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB HotpotQA
+ config: default
+ split: test
+ revision: ab518f4d6fcca38d87c25209f94beba119d02014
+ metrics:
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+ value: 41.789
+ - type: map_at_10
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: mrr_at_100
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+ - type: mrr_at_1000
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+ - type: mrr_at_20
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+ - type: mrr_at_3
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+ - type: mrr_at_5
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+ - type: ndcg_at_1
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+ - type: ndcg_at_10
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+ - type: ndcg_at_100
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+ - type: ndcg_at_1000
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+ - type: ndcg_at_20
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+ - type: ndcg_at_3
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+ - type: ndcg_at_5
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+ - type: precision_at_3
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+ - type: recall_at_10
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+ - type: recall_at_100
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+ - type: recall_at_1000
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+ - type: recall_at_20
+ value: 69.892
+ - type: recall_at_3
+ value: 58.392999999999994
+ - type: recall_at_5
+ value: 62.242
+ - task:
+ type: Classification
+ dataset:
+ type: None
+ name: MTEB ImdbClassification
+ config: default
+ split: test
+ revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+ metrics:
+ - type: accuracy
+ value: 86.6108
+ - type: ap
+ value: 81.63890253106925
+ - type: f1
+ value: 86.54585789538082
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB MSMARCO
+ config: default
+ split: dev
+ revision: c5a29a104738b98a9e76336939199e264163d4a0
+ metrics:
+ - type: map_at_1
+ value: 22.407
+ - type: map_at_10
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+ - type: map_at_100
+ value: 35.808
+ - type: map_at_1000
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+ - type: map_at_20
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_1
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+ - type: mrr_at_10
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+ - type: mrr_at_100
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+ - type: mrr_at_1000
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+ - type: mrr_at_20
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+ - type: mrr_at_3
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+ - type: mrr_at_5
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+ - type: ndcg_at_1
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+ - type: ndcg_at_10
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+ - type: ndcg_at_100
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+ - type: ndcg_at_1000
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+ - type: recall_at_1000
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - task:
+ type: Classification
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+ type: None
+ name: MTEB MTOPDomainClassification (en)
+ config: en
+ split: test
+ revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+ metrics:
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+ value: 92.94573643410853
+ - type: f1
+ value: 92.73148878666994
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+ type: Classification
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+ type: None
+ name: MTEB MTOPIntentClassification (en)
+ config: en
+ split: test
+ revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+ metrics:
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+ value: 77.86137710898313
+ - type: f1
+ value: 60.360562463738724
+ - task:
+ type: Classification
+ dataset:
+ type: None
+ name: MTEB MassiveIntentClassification (en)
+ config: en
+ split: test
+ revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+ metrics:
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+ value: 73.83322125084062
+ - type: f1
+ value: 71.61864304680206
+ - task:
+ type: Classification
+ dataset:
+ type: None
+ name: MTEB MassiveScenarioClassification (en)
+ config: en
+ split: test
+ revision: 7d571f92784cd94a019292a1f45445077d0ef634
+ metrics:
+ - type: accuracy
+ value: 77.50504371217215
+ - type: f1
+ value: 77.52039268347185
+ - task:
+ type: Clustering
+ dataset:
+ type: None
+ name: MTEB MedrxivClusteringP2P
+ config: default
+ split: test
+ revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+ metrics:
+ - type: v_measure
+ value: 34.346952648910225
+ - type: v_measures
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+ - task:
+ type: Clustering
+ dataset:
+ type: None
+ name: MTEB MedrxivClusteringS2S
+ config: default
+ split: test
+ revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+ metrics:
+ - type: v_measure
+ value: 32.19992734583148
+ - type: v_measures
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+ - task:
+ type: Reranking
+ dataset:
+ type: None
+ name: MTEB MindSmallReranking
+ config: default
+ split: test
+ revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+ metrics:
+ - type: map
+ value: 30.62309561205373
+ - type: mrr
+ value: 31.707879717902554
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB NFCorpus
+ config: default
+ split: test
+ revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+ metrics:
+ - type: map_at_1
+ value: 5.668
+ - type: map_at_10
+ value: 12.225999999999999
+ - type: map_at_100
+ value: 15.122
+ - type: map_at_1000
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+ - type: map_at_3
+ value: 9.083
+ - type: map_at_5
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+ - type: mrr_at_1
+ value: 46.44
+ - type: mrr_at_10
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+ - type: mrr_at_100
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+ - type: mrr_at_1000
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+ - type: mrr_at_20
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+ - type: mrr_at_3
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+ - type: mrr_at_5
+ value: 52.647
+ - type: ndcg_at_1
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+ - type: ndcg_at_10
+ value: 33.395
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+ - type: ndcg_at_1000
+ value: 38.388
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+ - type: ndcg_at_3
+ value: 39.212
+ - type: ndcg_at_5
+ value: 36.611
+ - type: precision_at_1
+ value: 46.129999999999995
+ - type: precision_at_10
+ value: 24.334
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+ value: 7.553999999999999
+ - type: precision_at_1000
+ value: 1.994
+ - type: precision_at_20
+ value: 17.678
+ - type: precision_at_3
+ value: 36.326
+ - type: precision_at_5
+ value: 31.330999999999996
+ - type: recall_at_1
+ value: 5.668
+ - type: recall_at_10
+ value: 15.837000000000002
+ - type: recall_at_100
+ value: 29.845
+ - type: recall_at_1000
+ value: 60.563
+ - type: recall_at_20
+ value: 18.587999999999997
+ - type: recall_at_3
+ value: 10.096
+ - type: recall_at_5
+ value: 12.261
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB NQ
+ config: default
+ split: test
+ revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+ metrics:
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+ value: 39.335
+ - type: map_at_10
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+ value: 55.742000000000004
+ - type: map_at_1000
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+ - type: map_at_20
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+ - type: map_at_3
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+ - type: map_at_5
+ value: 53.388999999999996
+ - type: mrr_at_1
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+ - type: mrr_at_10
+ value: 57.419
+ - type: mrr_at_100
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+ - type: mrr_at_1000
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+ - type: mrr_at_20
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+ - type: mrr_at_3
+ value: 54.30199999999999
+ - type: mrr_at_5
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+ - type: ndcg_at_1
+ value: 43.974999999999994
+ - type: ndcg_at_10
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+ - type: ndcg_at_100
+ value: 65.326
+ - type: ndcg_at_1000
+ value: 65.862
+ - type: ndcg_at_20
+ value: 63.922999999999995
+ - type: ndcg_at_3
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+ - type: ndcg_at_5
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+ - type: precision_at_1
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+ - type: precision_at_10
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+ value: 1.153
+ - type: precision_at_1000
+ value: 0.12
+ - type: precision_at_20
+ value: 5.3
+ - type: precision_at_3
+ value: 24.392
+ - type: precision_at_5
+ value: 16.993
+ - type: recall_at_1
+ value: 39.335
+ - type: recall_at_10
+ value: 81.501
+ - type: recall_at_100
+ value: 94.851
+ - type: recall_at_1000
+ value: 98.817
+ - type: recall_at_20
+ value: 87.968
+ - type: recall_at_3
+ value: 62.795
+ - type: recall_at_5
+ value: 71.985
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB QuoraRetrieval
+ config: default
+ split: test
+ revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
+ metrics:
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+ value: 71.222
+ - type: map_at_10
+ value: 85.193
+ - type: map_at_100
+ value: 85.802
+ - type: map_at_1000
+ value: 85.81800000000001
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+ - type: map_at_3
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+ - type: map_at_5
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+ - type: mrr_at_10
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+ - type: mrr_at_100
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+ - type: mrr_at_1000
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+ - type: mrr_at_20
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+ - type: mrr_at_3
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+ - type: mrr_at_5
+ value: 87.825
+ - type: ndcg_at_1
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+ - type: ndcg_at_10
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+ - type: ndcg_at_100
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+ - type: ndcg_at_20
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+ - type: ndcg_at_5
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+ - type: precision_at_1000
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+ - type: precision_at_20
+ value: 7.116
+ - type: precision_at_3
+ value: 37.617
+ - type: precision_at_5
+ value: 24.776
+ - type: recall_at_1
+ value: 71.222
+ - type: recall_at_10
+ value: 95.73899999999999
+ - type: recall_at_100
+ value: 99.572
+ - type: recall_at_1000
+ value: 99.988
+ - type: recall_at_20
+ value: 97.725
+ - type: recall_at_3
+ value: 87.742
+ - type: recall_at_5
+ value: 92.23400000000001
+ - task:
+ type: Clustering
+ dataset:
+ type: None
+ name: MTEB RedditClustering
+ config: default
+ split: test
+ revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+ metrics:
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+ value: 56.502005725283524
+ - type: v_measures
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+ - task:
+ type: Clustering
+ dataset:
+ type: None
+ name: MTEB RedditClusteringP2P
+ config: default
+ split: test
+ revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
+ metrics:
+ - type: v_measure
+ value: 63.14989421688691
+ - type: v_measures
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0.7513770750973534]
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB SCIDOCS
+ config: default
+ split: test
+ revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
+ metrics:
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+ value: 4.4830000000000005
+ - type: map_at_10
+ value: 11.04
+ - type: map_at_100
+ value: 12.764000000000001
+ - type: map_at_1000
+ value: 13.04
+ - type: map_at_20
+ value: 11.953
+ - type: map_at_3
+ value: 8.125
+ - type: map_at_5
+ value: 9.565999999999999
+ - type: mrr_at_1
+ value: 22.1
+ - type: mrr_at_10
+ value: 32.494
+ - type: mrr_at_100
+ value: 33.525
+ - type: mrr_at_1000
+ value: 33.596
+ - type: mrr_at_20
+ value: 33.089
+ - type: mrr_at_3
+ value: 29.416999999999998
+ - type: mrr_at_5
+ value: 31.267
+ - type: ndcg_at_1
+ value: 22.1
+ - type: ndcg_at_10
+ value: 18.587
+ - type: ndcg_at_100
+ value: 25.482
+ - type: ndcg_at_1000
+ value: 30.581999999999997
+ - type: ndcg_at_20
+ value: 21.077
+ - type: ndcg_at_3
+ value: 18.165
+ - type: ndcg_at_5
+ value: 15.676000000000002
+ - type: precision_at_1
+ value: 22.1
+ - type: precision_at_10
+ value: 9.48
+ - type: precision_at_100
+ value: 1.942
+ - type: precision_at_1000
+ value: 0.316
+ - type: precision_at_20
+ value: 6.175
+ - type: precision_at_3
+ value: 17.033
+ - type: precision_at_5
+ value: 13.719999999999999
+ - type: recall_at_1
+ value: 4.4830000000000005
+ - type: recall_at_10
+ value: 19.208
+ - type: recall_at_100
+ value: 39.417
+ - type: recall_at_1000
+ value: 64.235
+ - type: recall_at_20
+ value: 25.057000000000002
+ - type: recall_at_3
+ value: 10.348
+ - type: recall_at_5
+ value: 13.893
+ - task:
+ type: STS
+ dataset:
+ type: None
+ name: MTEB SICK-R
+ config: default
+ split: test
+ revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
+ metrics:
+ - type: cos_sim_pearson
+ value: 83.50181312649208
+ - type: cos_sim_spearman
+ value: 79.92900705478993
+ - type: euclidean_pearson
+ value: 81.13482128094503
+ - type: euclidean_spearman
+ value: 79.92732266864367
+ - type: manhattan_pearson
+ value: 81.06702121654993
+ - type: manhattan_spearman
+ value: 79.86983106619135
+ - task:
+ type: STS
+ dataset:
+ type: None
+ name: MTEB STS12
+ config: default
+ split: test
+ revision: a0d554a64d88156834ff5ae9920b964011b16384
+ metrics:
+ - type: cos_sim_pearson
+ value: 83.85431681906961
+ - type: cos_sim_spearman
+ value: 77.61671419416626
+ - type: euclidean_pearson
+ value: 81.30538320520961
+ - type: euclidean_spearman
+ value: 77.62096481461272
+ - type: manhattan_pearson
+ value: 81.2306021173407
+ - type: manhattan_spearman
+ value: 77.58386300715222
+ - task:
+ type: STS
+ dataset:
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+ - task:
+ type: Clustering
+ dataset:
+ type: None
+ name: MTEB StackExchangeClusteringP2P
+ config: default
+ split: test
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+ metrics:
+ - type: v_measure
+ value: 33.36641413495258
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+ - task:
+ type: Reranking
+ dataset:
+ type: None
+ name: MTEB StackOverflowDupQuestions
+ config: default
+ split: test
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+ metrics:
+ - type: map
+ value: 51.19282080158746
+ - type: mrr
+ value: 51.871100713012474
+ - task:
+ type: Summarization
+ dataset:
+ type: None
+ name: MTEB SummEval
+ config: default
+ split: test
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+ metrics:
+ - type: cos_sim_pearson
+ value: 31.437664703708485
+ - type: cos_sim_spearman
+ value: 31.391119208581575
+ - type: dot_pearson
+ value: 31.19925970504054
+ - type: dot_spearman
+ value: 31.38087224016694
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB TRECCOVID
+ config: default
+ split: test
+ revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
+ metrics:
+ - type: map_at_1
+ value: 0.249
+ - type: map_at_10
+ value: 2.163
+ - type: map_at_100
+ value: 13.242999999999999
+ - type: map_at_1000
+ value: 30.866
+ - type: map_at_20
+ value: 3.9539999999999997
+ - type: map_at_3
+ value: 0.718
+ - type: map_at_5
+ value: 1.169
+ - type: mrr_at_1
+ value: 96.0
+ - type: mrr_at_10
+ value: 98.0
+ - type: mrr_at_100
+ value: 98.0
+ - type: mrr_at_1000
+ value: 98.0
+ - type: mrr_at_20
+ value: 98.0
+ - type: mrr_at_3
+ value: 98.0
+ - type: mrr_at_5
+ value: 98.0
+ - type: ndcg_at_1
+ value: 92.0
+ - type: ndcg_at_10
+ value: 84.147
+ - type: ndcg_at_100
+ value: 65.143
+ - type: ndcg_at_1000
+ value: 56.038
+ - type: ndcg_at_20
+ value: 80.869
+ - type: ndcg_at_3
+ value: 89.11200000000001
+ - type: ndcg_at_5
+ value: 87.199
+ - type: precision_at_1
+ value: 96.0
+ - type: precision_at_10
+ value: 87.8
+ - type: precision_at_100
+ value: 66.72
+ - type: precision_at_1000
+ value: 24.684
+ - type: precision_at_20
+ value: 84.3
+ - type: precision_at_3
+ value: 94.0
+ - type: precision_at_5
+ value: 91.2
+ - type: recall_at_1
+ value: 0.249
+ - type: recall_at_10
+ value: 2.284
+ - type: recall_at_100
+ value: 16.025
+ - type: recall_at_1000
+ value: 52.068999999999996
+ - type: recall_at_20
+ value: 4.3180000000000005
+ - type: recall_at_3
+ value: 0.738
+ - type: recall_at_5
+ value: 1.212
+ - task:
+ type: Retrieval
+ dataset:
+ type: None
+ name: MTEB Touche2020
+ config: default
+ split: test
+ revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+ metrics:
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+ value: 3.4520000000000004
+ - type: map_at_10
+ value: 13.045000000000002
+ - type: map_at_100
+ value: 19.442
+ - type: map_at_1000
+ value: 21.09
+ - type: map_at_20
+ value: 15.667
+ - type: map_at_3
+ value: 7.409000000000001
+ - type: map_at_5
+ value: 9.73
+ - type: mrr_at_1
+ value: 46.939
+ - type: mrr_at_10
+ value: 60.295
+ - type: mrr_at_100
+ value: 60.904
+ - type: mrr_at_1000
+ value: 60.919000000000004
+ - type: mrr_at_20
+ value: 60.77
+ - type: mrr_at_3
+ value: 58.50300000000001
+ - type: mrr_at_5
+ value: 59.014
+ - type: ndcg_at_1
+ value: 44.897999999999996
+ - type: ndcg_at_10
+ value: 31.911
+ - type: ndcg_at_100
+ value: 41.945
+ - type: ndcg_at_1000
+ value: 53.181999999999995
+ - type: ndcg_at_20
+ value: 31.505
+ - type: ndcg_at_3
+ value: 39.745000000000005
+ - type: ndcg_at_5
+ value: 35.528999999999996
+ - type: precision_at_1
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+ - type: precision_at_10
+ value: 26.531
+ - type: precision_at_100
+ value: 8.163
+ - type: precision_at_1000
+ value: 1.559
+ - type: precision_at_20
+ value: 19.387999999999998
+ - type: precision_at_3
+ value: 40.136
+ - type: precision_at_5
+ value: 33.878
+ - type: recall_at_1
+ value: 3.4520000000000004
+ - type: recall_at_10
+ value: 18.899
+ - type: recall_at_100
+ value: 50.207
+ - type: recall_at_1000
+ value: 83.871
+ - type: recall_at_20
+ value: 26.756999999999998
+ - type: recall_at_3
+ value: 8.729000000000001
+ - type: recall_at_5
+ value: 12.084999999999999
+ - task:
+ type: Classification
+ dataset:
+ type: None
+ name: MTEB ToxicConversationsClassification
+ config: default
+ split: test
+ revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
+ metrics:
+ - type: accuracy
+ value: 67.4560546875
+ - type: ap
+ value: 12.720403845355294
+ - type: f1
+ value: 51.76062666567839
+ - task:
+ type: Classification
+ dataset:
+ type: None
+ name: MTEB TweetSentimentExtractionClassification
+ config: default
+ split: test
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+ metrics:
+ - type: accuracy
+ value: 62.36276174306734
+ - type: f1
+ value: 62.69956906934332
+ - task:
+ type: Clustering
+ dataset:
+ type: None
+ name: MTEB TwentyNewsgroupsClustering
+ config: default
+ split: test
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+ metrics:
+ - type: v_measure
+ value: 49.473492910233965
+ - type: v_measures
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+ - task:
+ type: PairClassification
+ dataset:
+ type: None
+ name: MTEB TwitterSemEval2015
+ config: default
+ split: test
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+ metrics:
+ - type: cos_sim_accuracy
+ value: 85.75430649102938
+ - type: cos_sim_ap
+ value: 73.62842656477649
+ - type: cos_sim_f1
+ value: 67.76023680315738
+ - type: cos_sim_precision
+ value: 63.61741547012506
+ - type: cos_sim_recall
+ value: 72.4802110817942
+ - type: dot_accuracy
+ value: 85.7423854085951
+ - type: dot_ap
+ value: 73.59147637253723
+ - type: dot_f1
+ value: 67.69498693867396
+ - type: dot_precision
+ value: 64.03859731701577
+ - type: dot_recall
+ value: 71.79419525065963
+ - type: euclidean_accuracy
+ value: 85.7423854085951
+ - type: euclidean_ap
+ value: 73.6288990409654
+ - type: euclidean_f1
+ value: 67.80415430267064
+ - type: euclidean_precision
+ value: 63.79711493718009
+ - type: euclidean_recall
+ value: 72.34828496042216
+ - type: manhattan_accuracy
+ value: 85.69470107885796
+ - type: manhattan_ap
+ value: 73.49219614602531
+ - type: manhattan_f1
+ value: 67.60809797550613
+ - type: manhattan_precision
+ value: 64.22127255460589
+ - type: manhattan_recall
+ value: 71.37203166226914
+ - type: max_accuracy
+ value: 85.75430649102938
+ - type: max_ap
+ value: 73.6288990409654
+ - type: max_f1
+ value: 67.80415430267064
+ - task:
+ type: PairClassification
+ dataset:
+ type: None
+ name: MTEB TwitterURLCorpus
+ config: default
+ split: test
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+ metrics:
+ - type: cos_sim_accuracy
+ value: 89.08293553770326
+ - type: cos_sim_ap
+ value: 86.21246419992926
+ - type: cos_sim_f1
+ value: 78.49922526377924
+ - type: cos_sim_precision
+ value: 75.35769939084857
+ - type: cos_sim_recall
+ value: 81.9140745303357
+ - type: dot_accuracy
+ value: 89.08681647067955
+ - type: dot_ap
+ value: 86.19733517196862
+ - type: dot_f1
+ value: 78.51132446157838
+ - type: dot_precision
+ value: 75.70233755093287
+ - type: dot_recall
+ value: 81.53680320295658
+ - type: euclidean_accuracy
+ value: 89.07517367175069
+ - type: euclidean_ap
+ value: 86.21198725320203
+ - type: euclidean_f1
+ value: 78.49867139061116
+ - type: euclidean_precision
+ value: 75.38276155372839
+ - type: euclidean_recall
+ value: 81.88327687095781
+ - type: manhattan_accuracy
+ value: 89.0538285403811
+ - type: manhattan_ap
+ value: 86.17785515765131
+ - type: manhattan_f1
+ value: 78.48184098593084
+ - type: manhattan_precision
+ value: 74.34396308285694
+ - type: manhattan_recall
+ value: 83.10748383122882
+ - type: max_accuracy
+ value: 89.08681647067955
+ - type: max_ap
+ value: 86.21246419992926
+ - type: max_f1
+ value: 78.51132446157838
+license: apache-2.0
+language:
+- en
+base_model:
+- answerdotai/ModernBERT-base
+- nomic-ai/modernbert-embed-unsupervised
+---
-modernbert-embed-base
\ No newline at end of file
+# ModernBERT Embed
+
+[![image/png](modernbertembed.png)](https://huggingface.co/nomic-ai/modernbert-embed-base)
+
+ModernBERT Embed is an embedding model trained from [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), bringing the new advances of ModernBERT to embeddings!
+
+Trained on the [Nomic Embed](https://arxiv.org/abs/2402.01613) weakly-supervised and supervised datasets, `modernbert-embed` also supports Matryoshka Representation Learning dimensions of 256, reducing memory by 3x with minimal performance loss.
+
+## Performance
+
+| Model | Dimensions | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) |
+|-----------------------|------------|--------------|---------------------|-----------------|-------------------------|---------------|----------------|-----------|------------------|
+| nomic-embed-text-v1 | 768 | 62.4 | 74.1 | 43.9 | **85.2** | 55.7 | 52.8 | 82.1 | 30.1 |
+| nomic-embed-text-v1.5 | 768 | 62.28 | 73.55 | 43.93 | 84.61 | 55.78 | **53.01** | **81.94** | 30.4 |
+| modernbert-embed-base | 768 | **62.62** | **74.31** | **44.98** | 83.96 | **56.42** | 52.89 | 81.78 | **31.39** |
+| nomic-embed-text-v1.5 | 256 | 61.04 | 72.1 | 43.16 | 84.09 | 55.18 | 50.81 | 81.34 | 30.05 |
+| modernbert-embed-base | 256 | 61.17 | 72.40 | 43.82 | 83.45 | 55.69 | 50.62 | 81.12 | 31.27 |
+
+
+
+## Usage
+
+You can use these models directly with the transformers library. Until the next transformers release, doing so requires installing `transformers` from `main`:
+
+```bash
+pip install git+https://github.com/huggingface/transformers.git
+```
+
+Reminder, this model is trained similarly to Nomic Embed and **REQUIRES** prefixes to be added to the input. For more information, see the instructions in [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#task-instruction-prefixes).
+
+Most use cases, adding `search_query: ` to the query and `search_document: ` to the documents will be sufficient.
+
+### Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("nomic-ai/modernbert-embed-base")
+
+query_embeddings = model.encode([
+ "search_query: What is TSNE?",
+ "search_query: Who is Laurens van der Maaten?",
+])
+doc_embeddings = model.encode([
+ "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
+])
+print(query_embeddings.shape, doc_embeddings.shape)
+# (2, 768) (1, 768)
+
+similarities = model.similarity(query_embeddings, doc_embeddings)
+print(similarities)
+# tensor([[0.7214],
+# [0.3260]])
+```
+
+Click to see Sentence Transformers usage with Matryoshka Truncation
+
+In Sentence Transformers, you can truncate embeddings to a smaller dimension by using the `truncate_dim` parameter when loading the `SentenceTransformer` model.
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("nomic-ai/modernbert-embed-base", truncate_dim=256)
+
+query_embeddings = model.encode([
+ "search_query: What is TSNE?",
+ "search_query: Who is Laurens van der Maaten?",
+])
+doc_embeddings = model.encode([
+ "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
+])
+print(query_embeddings.shape, doc_embeddings.shape)
+# (2, 256) (1, 256)
+
+similarities = model.similarity(query_embeddings, doc_embeddings)
+print(similarities)
+# tensor([[0.7759],
+# [0.3419]])
+```
+
+Note the small differences compared to the full 768-dimensional similarities.
+
+
+
+### Transformers
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+
+
+def mean_pooling(model_output, attention_mask):
+ token_embeddings = model_output[0]
+ input_mask_expanded = (
+ attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+ )
+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
+ input_mask_expanded.sum(1), min=1e-9
+ )
+
+
+queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
+documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
+
+tokenizer = AutoTokenizer.from_pretrained("nomic-ai/modernbert-embed-base")
+model = AutoModel.from_pretrained("nomic-ai/modernbert-embed-base")
+
+encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
+encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
+
+with torch.no_grad():
+ queries_outputs = model(**encoded_queries)
+ documents_outputs = model(**encoded_documents)
+
+query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
+query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
+doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
+doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
+print(query_embeddings.shape, doc_embeddings.shape)
+# torch.Size([2, 768]) torch.Size([1, 768])
+
+similarities = query_embeddings @ doc_embeddings.T
+print(similarities)
+# tensor([[0.7214],
+# [0.3260]])
+```
+
+Click to see Transformers usage with Matryoshka Truncation
+
+In `transformers`, you can truncate embeddings to a smaller dimension by slicing the mean pooled embeddings, prior to normalization.
+
+```python
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModel
+
+
+def mean_pooling(model_output, attention_mask):
+ token_embeddings = model_output[0]
+ input_mask_expanded = (
+ attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+ )
+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
+ input_mask_expanded.sum(1), min=1e-9
+ )
+
+
+queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
+documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
+
+tokenizer = AutoTokenizer.from_pretrained(".")
+model = AutoModel.from_pretrained(".")
+truncate_dim = 256
+
+encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
+encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
+
+with torch.no_grad():
+ queries_outputs = model(**encoded_queries)
+ documents_outputs = model(**encoded_documents)
+
+query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
+query_embeddings = query_embeddings[:, :truncate_dim]
+query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
+doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
+doc_embeddings = doc_embeddings[:, :truncate_dim]
+doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
+print(query_embeddings.shape, doc_embeddings.shape)
+# torch.Size([2, 256]) torch.Size([1, 256])
+
+similarities = query_embeddings @ doc_embeddings.T
+print(similarities)
+# tensor([[0.7759],
+# [0.3419]])
+```
+
+Note the small differences compared to the full 768-dimensional similarities.
+
+
+
+### Transformers.js
+
+If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
+```bash
+npm i @huggingface/transformers
+```
+
+Then, you can compute embeddings as follows:
+
+```javascript
+import { pipeline, matmul } from '@huggingface/transformers';
+
+// Create a feature extraction pipeline
+const extractor = await pipeline(
+ "feature-extraction",
+ "nomic-ai/modernbert-embed-base",
+ { dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16"
+);
+
+// Embed queries and documents
+const query_embeddings = await extractor([
+ "search_query: What is TSNE?",
+ "search_query: Who is Laurens van der Maaten?",
+ ], { pooling: "mean", normalize: true },
+);
+const doc_embeddings = await extractor([
+ "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
+ ], { pooling: "mean", normalize: true },
+);
+
+// Compute similarity scores
+const similarities = await matmul(query_embeddings, doc_embeddings.transpose(1, 0));
+console.log(similarities.tolist()); // [[0.721383273601532], [0.3259955644607544]]
+```
+
+
+## Training
+
+Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
+
+[![image/webp](https://cdn-uploads.huggingface.co/production/uploads/607997c83a565c15675055b3/pjhJhuNyRfPagRd_c_iUz.webp)](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
+
+We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
+the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
+
+In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
+
+For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1).
+
+Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
+
+
+## Join the Nomic Community
+
+- Nomic: [https://nomic.ai](https://nomic.ai)
+- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
+- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
+
+## Citation
+
+If you find the model, dataset, or training code useful, please cite our work
+
+```bibtex
+@misc{nussbaum2024nomic,
+ title={Nomic Embed: Training a Reproducible Long Context Text Embedder},
+ author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
+ year={2024},
+ eprint={2402.01613},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL}
+}
+```
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..cacbb2f
--- /dev/null
+++ b/config.json
@@ -0,0 +1,46 @@
+{
+ "_name_or_path": "nomic-ai/modernbert-embed",
+ "architectures": [
+ "ModernBertModel"
+ ],
+ "attention_bias": false,
+ "attention_dropout": 0.0,
+ "bos_token_id": 50281,
+ "classifier_activation": "gelu",
+ "classifier_bias": false,
+ "classifier_dropout": 0.0,
+ "classifier_pooling": "mean",
+ "cls_token_id": 50281,
+ "decoder_bias": true,
+ "deterministic_flash_attn": false,
+ "embedding_dropout": 0.0,
+ "eos_token_id": 50282,
+ "global_attn_every_n_layers": 3,
+ "global_rope_theta": 160000.0,
+ "gradient_checkpointing": false,
+ "hidden_activation": "gelu",
+ "hidden_size": 768,
+ "initializer_cutoff_factor": 2.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 1152,
+ "layer_norm_eps": 1e-05,
+ "local_attention": 128,
+ "local_rope_theta": 10000.0,
+ "max_position_embeddings": 8192,
+ "mlp_bias": false,
+ "mlp_dropout": 0.0,
+ "model_type": "modernbert",
+ "norm_bias": false,
+ "norm_eps": 1e-05,
+ "num_attention_heads": 12,
+ "num_hidden_layers": 22,
+ "pad_token_id": 50283,
+ "position_embedding_type": "absolute",
+ "reference_compile": null,
+ "sep_token_id": 50282,
+ "sparse_pred_ignore_index": -100,
+ "sparse_prediction": false,
+ "torch_dtype": "float32",
+ "transformers_version": "4.48.0.dev0",
+ "vocab_size": 50368
+}
diff --git a/config_sentence_transformers.json b/config_sentence_transformers.json
new file mode 100644
index 0000000..05c66ec
--- /dev/null
+++ b/config_sentence_transformers.json
@@ -0,0 +1,10 @@
+{
+ "__version__": {
+ "sentence_transformers": "3.3.1",
+ "transformers": "4.48.0.dev0",
+ "pytorch": "2.4.1+cu121"
+ },
+ "prompts": {},
+ "default_prompt_name": null,
+ "similarity_fn_name": "cosine"
+}
\ No newline at end of file
diff --git a/model.safetensors b/model.safetensors
new file mode 100644
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--- /dev/null
+++ b/model.safetensors
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+oid sha256:69f22ccd1ee0971b6173678c4fcadd29a49a3f5b6b8a6927002418abe4bd5b9c
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+version https://git-lfs.github.com/spec/v1
+oid sha256:c97e3ae9104ae231d4f728dc6bdb70eb1f56c46a8bd4c7baf43bf882955607af
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diff --git a/modules.json b/modules.json
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+++ b/modules.json
@@ -0,0 +1,20 @@
+[
+ {
+ "idx": 0,
+ "name": "0",
+ "path": "",
+ "type": "sentence_transformers.models.Transformer"
+ },
+ {
+ "idx": 1,
+ "name": "1",
+ "path": "1_Pooling",
+ "type": "sentence_transformers.models.Pooling"
+ },
+ {
+ "idx": 2,
+ "name": "2",
+ "path": "2_Normalize",
+ "type": "sentence_transformers.models.Normalize"
+ }
+]
\ No newline at end of file
diff --git a/onnx/model.onnx b/onnx/model.onnx
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+oid sha256:3b9dbe455b29fa24f50e7aa7c3ae923b0ad08ea87530cf2fe8e28ebc57a6ddc7
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+ "bur",
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+ "Ġf",
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+ "47",
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+ "qual",
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+ "och",
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+ "Ġpro",
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+ "prising",
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+ "Ġplead",
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+ "Ġthat",
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+ [
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+ "č",
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+ [
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+ ],
+ [
+ "feren",
+ "ced"
+ ],
+ [
+ "ĠOut",
+ "comes"
+ ]
+ ]
+ }
+}
\ No newline at end of file
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000..708dcd4
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,945 @@
+{
+ "added_tokens_decoder": {
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+ "content": "|||IP_ADDRESS|||",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "1": {
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+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "50254": {
+ "content": " ",
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+ "normalized": true,
+ "rstrip": false,
+ "single_word": false,
+ "special": false
+ },
+ "50255": {
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+ "single_word": false,
+ "special": false
+ },
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+ "50257": {
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+ "single_word": false,
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