BAAI
/

bge-large-en

@@ -1,114 +1,2599 @@
-# baai-general-embedding-large-en-instruction
-Map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification,  clustering, or semantic search.
-It also can be used in vector databases for  LLMs.
-For more details please refer to our GitHub: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)
-## Model List
-|              Model              | Language | Description | query instruction for retrieval |
-|:-------------------------------|:--------:| :--------:| :--------:|
-|  [BAAI/baai-general-embedding-large-en-instruction](https://huggingface.co/BAAI/baai-general-embedding-large-en-instruction) |   English |  rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
-|  [BAAI/baai-general-embedding-large-zh-instruction](https://huggingface.co/BAAI/baai-general-embedding-large-zh-instruction) |   Chinese | rank **1st** in [C-MTEB]() bechmark | `为这个句子生成表示以用于检索相关文章：`  |
-|  [BAAI/baai-general-embedding-large-zh](https://huggingface.co/BAAI/baai-general-embedding-large-zh) |   Chinese | rank **2nd** in [C-MTEB]() bechmark | --  |
-## Evaluation Results
-- **C-MTEB**:
-We create a benchmark C-MTEB for Chinese text embedding which consists of  31 datasets from 6 tasks.
-More details and evaluation scripts see [evaluation](evaluation/README.md).
-| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
-|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
-| [**baai-general-embedding-large-zh-instruction**](https://huggingface.co/BAAI/baai-general-embedding-large-zh-instruction) | 1024 | **63.84** | **71.53** | **53.23** | **78.94** | 72.26 | 62.33 | 48.39 |
-| [baai-general-embedding-large-zh](https://huggingface.co/BAAI/baai-general-embedding-large-zh) | 1024 | 63.62 | 70.55 | 50.98 | 76.77 | **72.49** | **65.63** | **50.01** |
-| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 |
-| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 |  57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 |
-| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 |  53.02 | 52.0 | 40.61 | 69.56 | 67.38 | 54.28 | 45.68 |
-| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 |  44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 |
-| [text2vec](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 |  47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 |
-| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 |
-## Usage
-### Sentence-Transformers
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-```
-pip install -U sentence-transformers
-```
-Then you can use the model like this:
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["样例数据-1", "样例数据-2"]
-model = SentenceTransformer('BAAI/baai-general-embedding-large-zh-instruction')
-embeddings = model.encode(sentences, normalize_embeddings=True)
-print(embeddings)
-#For retrieval task, when you use the model whose name ends with `-instruction`
-#each query should start with a instruction.
-queries = ["手机开不了机怎么办？"]
-passages = ["样例段落-1", "样例段落-2"]
-instruction = "为这个句子生成表示以用于检索相关文章："
-model = SentenceTransformer('BAAI/baai-general-embedding-large-zh-instruction')
-q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
-p_embeddings = model.encode(passages, normalize_embeddings=True)
-scores = q_embeddings @ p_embeddings.T
-```
-### HuggingFace Transformers
-Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
-```python
-from transformers import AutoTokenizer, AutoModel
-import torch
-# Sentences we want sentence embeddings for
-sentences = ["样例数据-1", "样例数据-2"]
-# Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('BAAI/baai-general-embedding-large-en-instruction')
-model = AutoModel.from_pretrained('BAAI/baai-general-embedding-large-en-instruction')
-# Tokenize sentences
-encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-# For retrieval task, need to add an instruction to query when using the "*-instruction" model.
-# encoded_input = tokenizer(["为这个句子生成表示以用于检索相关文章：" + query for query in queries], padding=True, truncation=True, return_tensors='pt')
-# Compute token embeddings
-with torch.no_grad():
-    model_output = model(**encoded_input)
-    # Perform pooling. In this case, cls pooling.
-    sentence_embeddings = model_output[0][:, 0]
-# normalize embeddings
-sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
-print("Sentence embeddings:")
-print(sentence_embeddings)
-```
-### Retrieval Task
-For retrieval task, when you use the model whose name ends with `-instruction`
-each query should start with a instruction.
-```python
-from sentence_transformers import SentenceTransformer
-queries = ["手机开不了机怎么办？"]
-passages = ["样例段落-1", "样例段落-2"]
-instruction = "Represent this sentence for searching relevant passages: "
-model = SentenceTransformer('BAAI/baai-general-embedding-large-en-instruction')
-q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
-p_embeddings = model.encode(passages, normalize_embeddings=True)
-scores = q_embeddings @ p_embeddings.T
-```
-## Limitations
-This model only works for Chinese texts and long texts will be truncated to a maximum of 512 tokens.

+---
+tags:
+- mteb
+model-index:
+- name: bge-large-en
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 76.08955223880598
+    - type: ap
+      value: 38.998374408322256
+    - type: f1
+      value: 70.01942671241072
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 92.11545000000001
+    - type: ap
+      value: 88.54348935561237
+    - type: f1
+      value: 92.09140230508743
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 47.022
+    - type: f1
+      value: 46.6104601802574
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 39.972
+    - type: map_at_10
+      value: 54.874
+    - type: map_at_100
+      value: 55.53399999999999
+    - type: map_at_1000
+      value: 55.539
+    - type: map_at_3
+      value: 51.031000000000006
+    - type: map_at_5
+      value: 53.342999999999996
+    - type: mrr_at_1
+      value: 40.541
+    - type: mrr_at_10
+      value: 55.096000000000004
+    - type: mrr_at_100
+      value: 55.75599999999999
+    - type: mrr_at_1000
+      value: 55.761
+    - type: mrr_at_3
+      value: 51.221000000000004
+    - type: mrr_at_5
+      value: 53.568000000000005
+    - type: ndcg_at_1
+      value: 39.972
+    - type: ndcg_at_10
+      value: 62.456999999999994
+    - type: ndcg_at_100
+      value: 65.262
+    - type: ndcg_at_1000
+      value: 65.389
+    - type: ndcg_at_3
+      value: 54.673
+    - type: ndcg_at_5
+      value: 58.80499999999999
+    - type: precision_at_1
+      value: 39.972
+    - type: precision_at_10
+      value: 8.634
+    - type: precision_at_100
+      value: 0.9860000000000001
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 21.740000000000002
+    - type: precision_at_5
+      value: 15.036
+    - type: recall_at_1
+      value: 39.972
+    - type: recall_at_10
+      value: 86.344
+    - type: recall_at_100
+      value: 98.578
+    - type: recall_at_1000
+      value: 99.57300000000001
+    - type: recall_at_3
+      value: 65.22
+    - type: recall_at_5
+      value: 75.178
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 48.94652870403906
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 43.17257160340209
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 63.97867370559182
+    - type: mrr
+      value: 77.00820032537484
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.00986015960616
+    - type: cos_sim_spearman
+      value: 80.36387933827882
+    - type: euclidean_pearson
+      value: 80.32305287257296
+    - type: euclidean_spearman
+      value: 82.0524720308763
+    - type: manhattan_pearson
+      value: 80.19847473906454
+    - type: manhattan_spearman
+      value: 81.87957652506985
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 87.8603896103896
+    - type: f1
+      value: 87.84830089792524
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 41.36932844640705
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 38.34983239611985
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.257999999999996
+    - type: map_at_10
+      value: 42.937
+    - type: map_at_100
+      value: 44.406
+    - type: map_at_1000
+      value: 44.536
+    - type: map_at_3
+      value: 39.22
+    - type: map_at_5
+      value: 41.458
+    - type: mrr_at_1
+      value: 38.769999999999996
+    - type: mrr_at_10
+      value: 48.701
+    - type: mrr_at_100
+      value: 49.431000000000004
+    - type: mrr_at_1000
+      value: 49.476
+    - type: mrr_at_3
+      value: 45.875
+    - type: mrr_at_5
+      value: 47.67
+    - type: ndcg_at_1
+      value: 38.769999999999996
+    - type: ndcg_at_10
+      value: 49.35
+    - type: ndcg_at_100
+      value: 54.618
+    - type: ndcg_at_1000
+      value: 56.655
+    - type: ndcg_at_3
+      value: 43.826
+    - type: ndcg_at_5
+      value: 46.72
+    - type: precision_at_1
+      value: 38.769999999999996
+    - type: precision_at_10
+      value: 9.328
+    - type: precision_at_100
+      value: 1.484
+    - type: precision_at_1000
+      value: 0.196
+    - type: precision_at_3
+      value: 20.649
+    - type: precision_at_5
+      value: 15.25
+    - type: recall_at_1
+      value: 32.257999999999996
+    - type: recall_at_10
+      value: 61.849
+    - type: recall_at_100
+      value: 83.70400000000001
+    - type: recall_at_1000
+      value: 96.344
+    - type: recall_at_3
+      value: 46.037
+    - type: recall_at_5
+      value: 53.724000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.979
+    - type: map_at_10
+      value: 43.376999999999995
+    - type: map_at_100
+      value: 44.667
+    - type: map_at_1000
+      value: 44.794
+    - type: map_at_3
+      value: 40.461999999999996
+    - type: map_at_5
+      value: 42.138
+    - type: mrr_at_1
+      value: 41.146
+    - type: mrr_at_10
+      value: 49.575
+    - type: mrr_at_100
+      value: 50.187000000000005
+    - type: mrr_at_1000
+      value: 50.231
+    - type: mrr_at_3
+      value: 47.601
+    - type: mrr_at_5
+      value: 48.786
+    - type: ndcg_at_1
+      value: 41.146
+    - type: ndcg_at_10
+      value: 48.957
+    - type: ndcg_at_100
+      value: 53.296
+    - type: ndcg_at_1000
+      value: 55.254000000000005
+    - type: ndcg_at_3
+      value: 45.235
+    - type: ndcg_at_5
+      value: 47.014
+    - type: precision_at_1
+      value: 41.146
+    - type: precision_at_10
+      value: 9.107999999999999
+    - type: precision_at_100
+      value: 1.481
+    - type: precision_at_1000
+      value: 0.193
+    - type: precision_at_3
+      value: 21.783
+    - type: precision_at_5
+      value: 15.274
+    - type: recall_at_1
+      value: 32.979
+    - type: recall_at_10
+      value: 58.167
+    - type: recall_at_100
+      value: 76.374
+    - type: recall_at_1000
+      value: 88.836
+    - type: recall_at_3
+      value: 46.838
+    - type: recall_at_5
+      value: 52.006
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 40.326
+    - type: map_at_10
+      value: 53.468
+    - type: map_at_100
+      value: 54.454
+    - type: map_at_1000
+      value: 54.508
+    - type: map_at_3
+      value: 50.12799999999999
+    - type: map_at_5
+      value: 51.991
+    - type: mrr_at_1
+      value: 46.394999999999996
+    - type: mrr_at_10
+      value: 57.016999999999996
+    - type: mrr_at_100
+      value: 57.67099999999999
+    - type: mrr_at_1000
+      value: 57.699999999999996
+    - type: mrr_at_3
+      value: 54.65
+    - type: mrr_at_5
+      value: 56.101
+    - type: ndcg_at_1
+      value: 46.394999999999996
+    - type: ndcg_at_10
+      value: 59.507
+    - type: ndcg_at_100
+      value: 63.31099999999999
+    - type: ndcg_at_1000
+      value: 64.388
+    - type: ndcg_at_3
+      value: 54.04600000000001
+    - type: ndcg_at_5
+      value: 56.723
+    - type: precision_at_1
+      value: 46.394999999999996
+    - type: precision_at_10
+      value: 9.567
+    - type: precision_at_100
+      value: 1.234
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 24.117
+    - type: precision_at_5
+      value: 16.426
+    - type: recall_at_1
+      value: 40.326
+    - type: recall_at_10
+      value: 73.763
+    - type: recall_at_100
+      value: 89.927
+    - type: recall_at_1000
+      value: 97.509
+    - type: recall_at_3
+      value: 59.34
+    - type: recall_at_5
+      value: 65.915
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.661
+    - type: map_at_10
+      value: 35.522
+    - type: map_at_100
+      value: 36.619
+    - type: map_at_1000
+      value: 36.693999999999996
+    - type: map_at_3
+      value: 33.154
+    - type: map_at_5
+      value: 34.353
+    - type: mrr_at_1
+      value: 28.362
+    - type: mrr_at_10
+      value: 37.403999999999996
+    - type: mrr_at_100
+      value: 38.374
+    - type: mrr_at_1000
+      value: 38.428000000000004
+    - type: mrr_at_3
+      value: 35.235
+    - type: mrr_at_5
+      value: 36.269
+    - type: ndcg_at_1
+      value: 28.362
+    - type: ndcg_at_10
+      value: 40.431
+    - type: ndcg_at_100
+      value: 45.745999999999995
+    - type: ndcg_at_1000
+      value: 47.493
+    - type: ndcg_at_3
+      value: 35.733
+    - type: ndcg_at_5
+      value: 37.722
+    - type: precision_at_1
+      value: 28.362
+    - type: precision_at_10
+      value: 6.101999999999999
+    - type: precision_at_100
+      value: 0.922
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 15.140999999999998
+    - type: precision_at_5
+      value: 10.305
+    - type: recall_at_1
+      value: 26.661
+    - type: recall_at_10
+      value: 53.675
+    - type: recall_at_100
+      value: 77.891
+    - type: recall_at_1000
+      value: 90.72
+    - type: recall_at_3
+      value: 40.751
+    - type: recall_at_5
+      value: 45.517
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.886
+    - type: map_at_10
+      value: 27.288
+    - type: map_at_100
+      value: 28.327999999999996
+    - type: map_at_1000
+      value: 28.438999999999997
+    - type: map_at_3
+      value: 24.453
+    - type: map_at_5
+      value: 25.959
+    - type: mrr_at_1
+      value: 23.134
+    - type: mrr_at_10
+      value: 32.004
+    - type: mrr_at_100
+      value: 32.789
+    - type: mrr_at_1000
+      value: 32.857
+    - type: mrr_at_3
+      value: 29.084
+    - type: mrr_at_5
+      value: 30.614
+    - type: ndcg_at_1
+      value: 23.134
+    - type: ndcg_at_10
+      value: 32.852
+    - type: ndcg_at_100
+      value: 37.972
+    - type: ndcg_at_1000
+      value: 40.656
+    - type: ndcg_at_3
+      value: 27.435
+    - type: ndcg_at_5
+      value: 29.823
+    - type: precision_at_1
+      value: 23.134
+    - type: precision_at_10
+      value: 6.032
+    - type: precision_at_100
+      value: 0.9950000000000001
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 13.017999999999999
+    - type: precision_at_5
+      value: 9.501999999999999
+    - type: recall_at_1
+      value: 18.886
+    - type: recall_at_10
+      value: 45.34
+    - type: recall_at_100
+      value: 67.947
+    - type: recall_at_1000
+      value: 86.924
+    - type: recall_at_3
+      value: 30.535
+    - type: recall_at_5
+      value: 36.451
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.994999999999997
+    - type: map_at_10
+      value: 40.04
+    - type: map_at_100
+      value: 41.435
+    - type: map_at_1000
+      value: 41.537
+    - type: map_at_3
+      value: 37.091
+    - type: map_at_5
+      value: 38.802
+    - type: mrr_at_1
+      value: 35.034
+    - type: mrr_at_10
+      value: 45.411
+    - type: mrr_at_100
+      value: 46.226
+    - type: mrr_at_1000
+      value: 46.27
+    - type: mrr_at_3
+      value: 43.086
+    - type: mrr_at_5
+      value: 44.452999999999996
+    - type: ndcg_at_1
+      value: 35.034
+    - type: ndcg_at_10
+      value: 46.076
+    - type: ndcg_at_100
+      value: 51.483000000000004
+    - type: ndcg_at_1000
+      value: 53.433
+    - type: ndcg_at_3
+      value: 41.304
+    - type: ndcg_at_5
+      value: 43.641999999999996
+    - type: precision_at_1
+      value: 35.034
+    - type: precision_at_10
+      value: 8.258000000000001
+    - type: precision_at_100
+      value: 1.268
+    - type: precision_at_1000
+      value: 0.161
+    - type: precision_at_3
+      value: 19.57
+    - type: precision_at_5
+      value: 13.782
+    - type: recall_at_1
+      value: 28.994999999999997
+    - type: recall_at_10
+      value: 58.538000000000004
+    - type: recall_at_100
+      value: 80.72399999999999
+    - type: recall_at_1000
+      value: 93.462
+    - type: recall_at_3
+      value: 45.199
+    - type: recall_at_5
+      value: 51.237
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.795
+    - type: map_at_10
+      value: 34.935
+    - type: map_at_100
+      value: 36.306
+    - type: map_at_1000
+      value: 36.417
+    - type: map_at_3
+      value: 31.831
+    - type: map_at_5
+      value: 33.626
+    - type: mrr_at_1
+      value: 30.479
+    - type: mrr_at_10
+      value: 40.225
+    - type: mrr_at_100
+      value: 41.055
+    - type: mrr_at_1000
+      value: 41.114
+    - type: mrr_at_3
+      value: 37.538
+    - type: mrr_at_5
+      value: 39.073
+    - type: ndcg_at_1
+      value: 30.479
+    - type: ndcg_at_10
+      value: 40.949999999999996
+    - type: ndcg_at_100
+      value: 46.525
+    - type: ndcg_at_1000
+      value: 48.892
+    - type: ndcg_at_3
+      value: 35.79
+    - type: ndcg_at_5
+      value: 38.237
+    - type: precision_at_1
+      value: 30.479
+    - type: precision_at_10
+      value: 7.6259999999999994
+    - type: precision_at_100
+      value: 1.203
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 17.199
+    - type: precision_at_5
+      value: 12.466000000000001
+    - type: recall_at_1
+      value: 24.795
+    - type: recall_at_10
+      value: 53.421
+    - type: recall_at_100
+      value: 77.189
+    - type: recall_at_1000
+      value: 93.407
+    - type: recall_at_3
+      value: 39.051
+    - type: recall_at_5
+      value: 45.462
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.853499999999997
+    - type: map_at_10
+      value: 36.20433333333333
+    - type: map_at_100
+      value: 37.40391666666667
+    - type: map_at_1000
+      value: 37.515
+    - type: map_at_3
+      value: 33.39975
+    - type: map_at_5
+      value: 34.9665
+    - type: mrr_at_1
+      value: 31.62666666666667
+    - type: mrr_at_10
+      value: 40.436749999999996
+    - type: mrr_at_100
+      value: 41.260333333333335
+    - type: mrr_at_1000
+      value: 41.31525
+    - type: mrr_at_3
+      value: 38.06733333333332
+    - type: mrr_at_5
+      value: 39.41541666666667
+    - type: ndcg_at_1
+      value: 31.62666666666667
+    - type: ndcg_at_10
+      value: 41.63341666666667
+    - type: ndcg_at_100
+      value: 46.704166666666666
+    - type: ndcg_at_1000
+      value: 48.88483333333335
+    - type: ndcg_at_3
+      value: 36.896
+    - type: ndcg_at_5
+      value: 39.11891666666667
+    - type: precision_at_1
+      value: 31.62666666666667
+    - type: precision_at_10
+      value: 7.241083333333333
+    - type: precision_at_100
+      value: 1.1488333333333334
+    - type: precision_at_1000
+      value: 0.15250000000000002
+    - type: precision_at_3
+      value: 16.908333333333335
+    - type: precision_at_5
+      value: 11.942833333333333
+    - type: recall_at_1
+      value: 26.853499999999997
+    - type: recall_at_10
+      value: 53.461333333333336
+    - type: recall_at_100
+      value: 75.63633333333333
+    - type: recall_at_1000
+      value: 90.67016666666666
+    - type: recall_at_3
+      value: 40.24241666666667
+    - type: recall_at_5
+      value: 45.98608333333333
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.241999999999997
+    - type: map_at_10
+      value: 31.863999999999997
+    - type: map_at_100
+      value: 32.835
+    - type: map_at_1000
+      value: 32.928000000000004
+    - type: map_at_3
+      value: 29.694
+    - type: map_at_5
+      value: 30.978
+    - type: mrr_at_1
+      value: 28.374
+    - type: mrr_at_10
+      value: 34.814
+    - type: mrr_at_100
+      value: 35.596
+    - type: mrr_at_1000
+      value: 35.666
+    - type: mrr_at_3
+      value: 32.745000000000005
+    - type: mrr_at_5
+      value: 34.049
+    - type: ndcg_at_1
+      value: 28.374
+    - type: ndcg_at_10
+      value: 35.969
+    - type: ndcg_at_100
+      value: 40.708
+    - type: ndcg_at_1000
+      value: 43.08
+    - type: ndcg_at_3
+      value: 31.968999999999998
+    - type: ndcg_at_5
+      value: 34.069
+    - type: precision_at_1
+      value: 28.374
+    - type: precision_at_10
+      value: 5.583
+    - type: precision_at_100
+      value: 0.8630000000000001
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 13.547999999999998
+    - type: precision_at_5
+      value: 9.447999999999999
+    - type: recall_at_1
+      value: 25.241999999999997
+    - type: recall_at_10
+      value: 45.711
+    - type: recall_at_100
+      value: 67.482
+    - type: recall_at_1000
+      value: 85.13300000000001
+    - type: recall_at_3
+      value: 34.622
+    - type: recall_at_5
+      value: 40.043
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.488999999999997
+    - type: map_at_10
+      value: 25.142999999999997
+    - type: map_at_100
+      value: 26.244
+    - type: map_at_1000
+      value: 26.363999999999997
+    - type: map_at_3
+      value: 22.654
+    - type: map_at_5
+      value: 24.017
+    - type: mrr_at_1
+      value: 21.198
+    - type: mrr_at_10
+      value: 28.903000000000002
+    - type: mrr_at_100
+      value: 29.860999999999997
+    - type: mrr_at_1000
+      value: 29.934
+    - type: mrr_at_3
+      value: 26.634999999999998
+    - type: mrr_at_5
+      value: 27.903
+    - type: ndcg_at_1
+      value: 21.198
+    - type: ndcg_at_10
+      value: 29.982999999999997
+    - type: ndcg_at_100
+      value: 35.275
+    - type: ndcg_at_1000
+      value: 38.074000000000005
+    - type: ndcg_at_3
+      value: 25.502999999999997
+    - type: ndcg_at_5
+      value: 27.557
+    - type: precision_at_1
+      value: 21.198
+    - type: precision_at_10
+      value: 5.502
+    - type: precision_at_100
+      value: 0.942
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 12.044
+    - type: precision_at_5
+      value: 8.782
+    - type: recall_at_1
+      value: 17.488999999999997
+    - type: recall_at_10
+      value: 40.821000000000005
+    - type: recall_at_100
+      value: 64.567
+    - type: recall_at_1000
+      value: 84.452
+    - type: recall_at_3
+      value: 28.351
+    - type: recall_at_5
+      value: 33.645
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.066000000000003
+    - type: map_at_10
+      value: 36.134
+    - type: map_at_100
+      value: 37.285000000000004
+    - type: map_at_1000
+      value: 37.389
+    - type: map_at_3
+      value: 33.522999999999996
+    - type: map_at_5
+      value: 34.905
+    - type: mrr_at_1
+      value: 31.436999999999998
+    - type: mrr_at_10
+      value: 40.225
+    - type: mrr_at_100
+      value: 41.079
+    - type: mrr_at_1000
+      value: 41.138000000000005
+    - type: mrr_at_3
+      value: 38.074999999999996
+    - type: mrr_at_5
+      value: 39.190000000000005
+    - type: ndcg_at_1
+      value: 31.436999999999998
+    - type: ndcg_at_10
+      value: 41.494
+    - type: ndcg_at_100
+      value: 46.678999999999995
+    - type: ndcg_at_1000
+      value: 48.964
+    - type: ndcg_at_3
+      value: 36.828
+    - type: ndcg_at_5
+      value: 38.789
+    - type: precision_at_1
+      value: 31.436999999999998
+    - type: precision_at_10
+      value: 6.931
+    - type: precision_at_100
+      value: 1.072
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 16.729
+    - type: precision_at_5
+      value: 11.567
+    - type: recall_at_1
+      value: 27.066000000000003
+    - type: recall_at_10
+      value: 53.705000000000005
+    - type: recall_at_100
+      value: 75.968
+    - type: recall_at_1000
+      value: 91.937
+    - type: recall_at_3
+      value: 40.865
+    - type: recall_at_5
+      value: 45.739999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.979000000000003
+    - type: map_at_10
+      value: 32.799
+    - type: map_at_100
+      value: 34.508
+    - type: map_at_1000
+      value: 34.719
+    - type: map_at_3
+      value: 29.947000000000003
+    - type: map_at_5
+      value: 31.584
+    - type: mrr_at_1
+      value: 30.237000000000002
+    - type: mrr_at_10
+      value: 37.651
+    - type: mrr_at_100
+      value: 38.805
+    - type: mrr_at_1000
+      value: 38.851
+    - type: mrr_at_3
+      value: 35.046
+    - type: mrr_at_5
+      value: 36.548
+    - type: ndcg_at_1
+      value: 30.237000000000002
+    - type: ndcg_at_10
+      value: 38.356
+    - type: ndcg_at_100
+      value: 44.906
+    - type: ndcg_at_1000
+      value: 47.299
+    - type: ndcg_at_3
+      value: 33.717999999999996
+    - type: ndcg_at_5
+      value: 35.946
+    - type: precision_at_1
+      value: 30.237000000000002
+    - type: precision_at_10
+      value: 7.292
+    - type: precision_at_100
+      value: 1.496
+    - type: precision_at_1000
+      value: 0.23600000000000002
+    - type: precision_at_3
+      value: 15.547
+    - type: precision_at_5
+      value: 11.344
+    - type: recall_at_1
+      value: 24.979000000000003
+    - type: recall_at_10
+      value: 48.624
+    - type: recall_at_100
+      value: 77.932
+    - type: recall_at_1000
+      value: 92.66499999999999
+    - type: recall_at_3
+      value: 35.217
+    - type: recall_at_5
+      value: 41.394
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.566
+    - type: map_at_10
+      value: 30.945
+    - type: map_at_100
+      value: 31.759999999999998
+    - type: map_at_1000
+      value: 31.855
+    - type: map_at_3
+      value: 28.64
+    - type: map_at_5
+      value: 29.787000000000003
+    - type: mrr_at_1
+      value: 24.954
+    - type: mrr_at_10
+      value: 33.311
+    - type: mrr_at_100
+      value: 34.050000000000004
+    - type: mrr_at_1000
+      value: 34.117999999999995
+    - type: mrr_at_3
+      value: 31.238
+    - type: mrr_at_5
+      value: 32.329
+    - type: ndcg_at_1
+      value: 24.954
+    - type: ndcg_at_10
+      value: 35.676
+    - type: ndcg_at_100
+      value: 39.931
+    - type: ndcg_at_1000
+      value: 42.43
+    - type: ndcg_at_3
+      value: 31.365
+    - type: ndcg_at_5
+      value: 33.184999999999995
+    - type: precision_at_1
+      value: 24.954
+    - type: precision_at_10
+      value: 5.564
+    - type: precision_at_100
+      value: 0.826
+    - type: precision_at_1000
+      value: 0.116
+    - type: precision_at_3
+      value: 13.555
+    - type: precision_at_5
+      value: 9.168
+    - type: recall_at_1
+      value: 22.566
+    - type: recall_at_10
+      value: 47.922
+    - type: recall_at_100
+      value: 67.931
+    - type: recall_at_1000
+      value: 86.653
+    - type: recall_at_3
+      value: 36.103
+    - type: recall_at_5
+      value: 40.699000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.950000000000003
+    - type: map_at_10
+      value: 28.612
+    - type: map_at_100
+      value: 30.476999999999997
+    - type: map_at_1000
+      value: 30.674
+    - type: map_at_3
+      value: 24.262
+    - type: map_at_5
+      value: 26.554
+    - type: mrr_at_1
+      value: 38.241
+    - type: mrr_at_10
+      value: 50.43
+    - type: mrr_at_100
+      value: 51.059
+    - type: mrr_at_1000
+      value: 51.090999999999994
+    - type: mrr_at_3
+      value: 47.514
+    - type: mrr_at_5
+      value: 49.246
+    - type: ndcg_at_1
+      value: 38.241
+    - type: ndcg_at_10
+      value: 38.218
+    - type: ndcg_at_100
+      value: 45.003
+    - type: ndcg_at_1000
+      value: 48.269
+    - type: ndcg_at_3
+      value: 32.568000000000005
+    - type: ndcg_at_5
+      value: 34.400999999999996
+    - type: precision_at_1
+      value: 38.241
+    - type: precision_at_10
+      value: 11.674
+    - type: precision_at_100
+      value: 1.913
+    - type: precision_at_1000
+      value: 0.252
+    - type: precision_at_3
+      value: 24.387
+    - type: precision_at_5
+      value: 18.163
+    - type: recall_at_1
+      value: 16.950000000000003
+    - type: recall_at_10
+      value: 43.769000000000005
+    - type: recall_at_100
+      value: 66.875
+    - type: recall_at_1000
+      value: 84.92699999999999
+    - type: recall_at_3
+      value: 29.353
+    - type: recall_at_5
+      value: 35.467
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 9.276
+    - type: map_at_10
+      value: 20.848
+    - type: map_at_100
+      value: 29.804000000000002
+    - type: map_at_1000
+      value: 31.398
+    - type: map_at_3
+      value: 14.886
+    - type: map_at_5
+      value: 17.516000000000002
+    - type: mrr_at_1
+      value: 71.0
+    - type: mrr_at_10
+      value: 78.724
+    - type: mrr_at_100
+      value: 78.976
+    - type: mrr_at_1000
+      value: 78.986
+    - type: mrr_at_3
+      value: 77.333
+    - type: mrr_at_5
+      value: 78.021
+    - type: ndcg_at_1
+      value: 57.875
+    - type: ndcg_at_10
+      value: 43.855
+    - type: ndcg_at_100
+      value: 48.99
+    - type: ndcg_at_1000
+      value: 56.141
+    - type: ndcg_at_3
+      value: 48.914
+    - type: ndcg_at_5
+      value: 45.961
+    - type: precision_at_1
+      value: 71.0
+    - type: precision_at_10
+      value: 34.575
+    - type: precision_at_100
+      value: 11.182
+    - type: precision_at_1000
+      value: 2.044
+    - type: precision_at_3
+      value: 52.5
+    - type: precision_at_5
+      value: 44.2
+    - type: recall_at_1
+      value: 9.276
+    - type: recall_at_10
+      value: 26.501
+    - type: recall_at_100
+      value: 55.72899999999999
+    - type: recall_at_1000
+      value: 78.532
+    - type: recall_at_3
+      value: 16.365
+    - type: recall_at_5
+      value: 20.154
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 53.400000000000006
+    - type: f1
+      value: 48.55192863364752
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 73.405
+    - type: map_at_10
+      value: 82.822
+    - type: map_at_100
+      value: 83.042
+    - type: map_at_1000
+      value: 83.055
+    - type: map_at_3
+      value: 81.65299999999999
+    - type: map_at_5
+      value: 82.431
+    - type: mrr_at_1
+      value: 79.178
+    - type: mrr_at_10
+      value: 87.02
+    - type: mrr_at_100
+      value: 87.095
+    - type: mrr_at_1000
+      value: 87.09700000000001
+    - type: mrr_at_3
+      value: 86.309
+    - type: mrr_at_5
+      value: 86.824
+    - type: ndcg_at_1
+      value: 79.178
+    - type: ndcg_at_10
+      value: 86.72
+    - type: ndcg_at_100
+      value: 87.457
+    - type: ndcg_at_1000
+      value: 87.691
+    - type: ndcg_at_3
+      value: 84.974
+    - type: ndcg_at_5
+      value: 86.032
+    - type: precision_at_1
+      value: 79.178
+    - type: precision_at_10
+      value: 10.548
+    - type: precision_at_100
+      value: 1.113
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 32.848
+    - type: precision_at_5
+      value: 20.45
+    - type: recall_at_1
+      value: 73.405
+    - type: recall_at_10
+      value: 94.39699999999999
+    - type: recall_at_100
+      value: 97.219
+    - type: recall_at_1000
+      value: 98.675
+    - type: recall_at_3
+      value: 89.679
+    - type: recall_at_5
+      value: 92.392
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.651
+    - type: map_at_10
+      value: 36.886
+    - type: map_at_100
+      value: 38.811
+    - type: map_at_1000
+      value: 38.981
+    - type: map_at_3
+      value: 32.538
+    - type: map_at_5
+      value: 34.763
+    - type: mrr_at_1
+      value: 44.444
+    - type: mrr_at_10
+      value: 53.168000000000006
+    - type: mrr_at_100
+      value: 53.839000000000006
+    - type: mrr_at_1000
+      value: 53.869
+    - type: mrr_at_3
+      value: 50.54
+    - type: mrr_at_5
+      value: 52.068000000000005
+    - type: ndcg_at_1
+      value: 44.444
+    - type: ndcg_at_10
+      value: 44.994
+    - type: ndcg_at_100
+      value: 51.599
+    - type: ndcg_at_1000
+      value: 54.339999999999996
+    - type: ndcg_at_3
+      value: 41.372
+    - type: ndcg_at_5
+      value: 42.149
+    - type: precision_at_1
+      value: 44.444
+    - type: precision_at_10
+      value: 12.407
+    - type: precision_at_100
+      value: 1.9269999999999998
+    - type: precision_at_1000
+      value: 0.242
+    - type: precision_at_3
+      value: 27.726
+    - type: precision_at_5
+      value: 19.814999999999998
+    - type: recall_at_1
+      value: 22.651
+    - type: recall_at_10
+      value: 52.075
+    - type: recall_at_100
+      value: 76.51400000000001
+    - type: recall_at_1000
+      value: 92.852
+    - type: recall_at_3
+      value: 37.236000000000004
+    - type: recall_at_5
+      value: 43.175999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 40.777
+    - type: map_at_10
+      value: 66.79899999999999
+    - type: map_at_100
+      value: 67.65299999999999
+    - type: map_at_1000
+      value: 67.706
+    - type: map_at_3
+      value: 63.352
+    - type: map_at_5
+      value: 65.52900000000001
+    - type: mrr_at_1
+      value: 81.553
+    - type: mrr_at_10
+      value: 86.983
+    - type: mrr_at_100
+      value: 87.132
+    - type: mrr_at_1000
+      value: 87.136
+    - type: mrr_at_3
+      value: 86.156
+    - type: mrr_at_5
+      value: 86.726
+    - type: ndcg_at_1
+      value: 81.553
+    - type: ndcg_at_10
+      value: 74.64
+    - type: ndcg_at_100
+      value: 77.459
+    - type: ndcg_at_1000
+      value: 78.43
+    - type: ndcg_at_3
+      value: 69.878
+    - type: ndcg_at_5
+      value: 72.59400000000001
+    - type: precision_at_1
+      value: 81.553
+    - type: precision_at_10
+      value: 15.654000000000002
+    - type: precision_at_100
+      value: 1.783
+    - type: precision_at_1000
+      value: 0.191
+    - type: precision_at_3
+      value: 45.199
+    - type: precision_at_5
+      value: 29.267
+    - type: recall_at_1
+      value: 40.777
+    - type: recall_at_10
+      value: 78.271
+    - type: recall_at_100
+      value: 89.129
+    - type: recall_at_1000
+      value: 95.49
+    - type: recall_at_3
+      value: 67.79899999999999
+    - type: recall_at_5
+      value: 73.167
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 93.724
+    - type: ap
+      value: 90.62951630330716
+    - type: f1
+      value: 93.7196637416671
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.301
+    - type: map_at_10
+      value: 35.657
+    - type: map_at_100
+      value: 36.797000000000004
+    - type: map_at_1000
+      value: 36.844
+    - type: map_at_3
+      value: 31.743
+    - type: map_at_5
+      value: 34.003
+    - type: mrr_at_1
+      value: 23.854
+    - type: mrr_at_10
+      value: 36.242999999999995
+    - type: mrr_at_100
+      value: 37.32
+    - type: mrr_at_1000
+      value: 37.361
+    - type: mrr_at_3
+      value: 32.4
+    - type: mrr_at_5
+      value: 34.634
+    - type: ndcg_at_1
+      value: 23.868000000000002
+    - type: ndcg_at_10
+      value: 42.589
+    - type: ndcg_at_100
+      value: 48.031
+    - type: ndcg_at_1000
+      value: 49.189
+    - type: ndcg_at_3
+      value: 34.649
+    - type: ndcg_at_5
+      value: 38.676
+    - type: precision_at_1
+      value: 23.868000000000002
+    - type: precision_at_10
+      value: 6.6850000000000005
+    - type: precision_at_100
+      value: 0.9400000000000001
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.651
+    - type: precision_at_5
+      value: 10.834000000000001
+    - type: recall_at_1
+      value: 23.301
+    - type: recall_at_10
+      value: 63.88700000000001
+    - type: recall_at_100
+      value: 88.947
+    - type: recall_at_1000
+      value: 97.783
+    - type: recall_at_3
+      value: 42.393
+    - type: recall_at_5
+      value: 52.036
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 94.67624259005929
+    - type: f1
+      value: 94.43953546498719
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 79.89512083903328
+    - type: f1
+      value: 61.64282062596609
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 79.58305312710155
+    - type: f1
+      value: 77.32463609252174
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 81.8897108271688
+    - type: f1
+      value: 81.67334926430276
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 36.31529875009507
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 31.734233714415073
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 30.994501713009452
+    - type: mrr
+      value: 32.13512850703073
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.603000000000001
+    - type: map_at_10
+      value: 13.767999999999999
+    - type: map_at_100
+      value: 17.197000000000003
+    - type: map_at_1000
+      value: 18.615000000000002
+    - type: map_at_3
+      value: 10.567
+    - type: map_at_5
+      value: 12.078999999999999
+    - type: mrr_at_1
+      value: 44.891999999999996
+    - type: mrr_at_10
+      value: 53.75299999999999
+    - type: mrr_at_100
+      value: 54.35
+    - type: mrr_at_1000
+      value: 54.388000000000005
+    - type: mrr_at_3
+      value: 51.495999999999995
+    - type: mrr_at_5
+      value: 52.688
+    - type: ndcg_at_1
+      value: 43.189
+    - type: ndcg_at_10
+      value: 34.567
+    - type: ndcg_at_100
+      value: 32.273
+    - type: ndcg_at_1000
+      value: 41.321999999999996
+    - type: ndcg_at_3
+      value: 40.171
+    - type: ndcg_at_5
+      value: 37.502
+    - type: precision_at_1
+      value: 44.582
+    - type: precision_at_10
+      value: 25.139
+    - type: precision_at_100
+      value: 7.739999999999999
+    - type: precision_at_1000
+      value: 2.054
+    - type: precision_at_3
+      value: 37.152
+    - type: precision_at_5
+      value: 31.826999999999998
+    - type: recall_at_1
+      value: 6.603000000000001
+    - type: recall_at_10
+      value: 17.023
+    - type: recall_at_100
+      value: 32.914
+    - type: recall_at_1000
+      value: 64.44800000000001
+    - type: recall_at_3
+      value: 11.457
+    - type: recall_at_5
+      value: 13.816
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.026000000000003
+    - type: map_at_10
+      value: 45.429
+    - type: map_at_100
+      value: 46.45
+    - type: map_at_1000
+      value: 46.478
+    - type: map_at_3
+      value: 41.147
+    - type: map_at_5
+      value: 43.627
+    - type: mrr_at_1
+      value: 33.951
+    - type: mrr_at_10
+      value: 47.953
+    - type: mrr_at_100
+      value: 48.731
+    - type: mrr_at_1000
+      value: 48.751
+    - type: mrr_at_3
+      value: 44.39
+    - type: mrr_at_5
+      value: 46.533
+    - type: ndcg_at_1
+      value: 33.951
+    - type: ndcg_at_10
+      value: 53.24100000000001
+    - type: ndcg_at_100
+      value: 57.599999999999994
+    - type: ndcg_at_1000
+      value: 58.270999999999994
+    - type: ndcg_at_3
+      value: 45.190999999999995
+    - type: ndcg_at_5
+      value: 49.339
+    - type: precision_at_1
+      value: 33.951
+    - type: precision_at_10
+      value: 8.856
+    - type: precision_at_100
+      value: 1.133
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 20.713
+    - type: precision_at_5
+      value: 14.838000000000001
+    - type: recall_at_1
+      value: 30.026000000000003
+    - type: recall_at_10
+      value: 74.512
+    - type: recall_at_100
+      value: 93.395
+    - type: recall_at_1000
+      value: 98.402
+    - type: recall_at_3
+      value: 53.677
+    - type: recall_at_5
+      value: 63.198
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 71.41300000000001
+    - type: map_at_10
+      value: 85.387
+    - type: map_at_100
+      value: 86.027
+    - type: map_at_1000
+      value: 86.041
+    - type: map_at_3
+      value: 82.543
+    - type: map_at_5
+      value: 84.304
+    - type: mrr_at_1
+      value: 82.35
+    - type: mrr_at_10
+      value: 88.248
+    - type: mrr_at_100
+      value: 88.348
+    - type: mrr_at_1000
+      value: 88.349
+    - type: mrr_at_3
+      value: 87.348
+    - type: mrr_at_5
+      value: 87.96300000000001
+    - type: ndcg_at_1
+      value: 82.37
+    - type: ndcg_at_10
+      value: 88.98
+    - type: ndcg_at_100
+      value: 90.16499999999999
+    - type: ndcg_at_1000
+      value: 90.239
+    - type: ndcg_at_3
+      value: 86.34100000000001
+    - type: ndcg_at_5
+      value: 87.761
+    - type: precision_at_1
+      value: 82.37
+    - type: precision_at_10
+      value: 13.471
+    - type: precision_at_100
+      value: 1.534
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.827
+    - type: precision_at_5
+      value: 24.773999999999997
+    - type: recall_at_1
+      value: 71.41300000000001
+    - type: recall_at_10
+      value: 95.748
+    - type: recall_at_100
+      value: 99.69200000000001
+    - type: recall_at_1000
+      value: 99.98
+    - type: recall_at_3
+      value: 87.996
+    - type: recall_at_5
+      value: 92.142
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 56.96878497780007
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 65.31371347128074
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.287
+    - type: map_at_10
+      value: 13.530000000000001
+    - type: map_at_100
+      value: 15.891
+    - type: map_at_1000
+      value: 16.245
+    - type: map_at_3
+      value: 9.612
+    - type: map_at_5
+      value: 11.672
+    - type: mrr_at_1
+      value: 26.0
+    - type: mrr_at_10
+      value: 37.335
+    - type: mrr_at_100
+      value: 38.443
+    - type: mrr_at_1000
+      value: 38.486
+    - type: mrr_at_3
+      value: 33.783
+    - type: mrr_at_5
+      value: 36.028
+    - type: ndcg_at_1
+      value: 26.0
+    - type: ndcg_at_10
+      value: 22.215
+    - type: ndcg_at_100
+      value: 31.101
+    - type: ndcg_at_1000
+      value: 36.809
+    - type: ndcg_at_3
+      value: 21.104
+    - type: ndcg_at_5
+      value: 18.759999999999998
+    - type: precision_at_1
+      value: 26.0
+    - type: precision_at_10
+      value: 11.43
+    - type: precision_at_100
+      value: 2.424
+    - type: precision_at_1000
+      value: 0.379
+    - type: precision_at_3
+      value: 19.7
+    - type: precision_at_5
+      value: 16.619999999999997
+    - type: recall_at_1
+      value: 5.287
+    - type: recall_at_10
+      value: 23.18
+    - type: recall_at_100
+      value: 49.208
+    - type: recall_at_1000
+      value: 76.85300000000001
+    - type: recall_at_3
+      value: 11.991999999999999
+    - type: recall_at_5
+      value: 16.85
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.87834913790886
+    - type: cos_sim_spearman
+      value: 81.04583513112122
+    - type: euclidean_pearson
+      value: 81.20484174558065
+    - type: euclidean_spearman
+      value: 80.76430832561769
+    - type: manhattan_pearson
+      value: 81.21416730978615
+    - type: manhattan_spearman
+      value: 80.7797637394211
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.56143998865157
+    - type: cos_sim_spearman
+      value: 79.75387012744471
+    - type: euclidean_pearson
+      value: 83.7877519997019
+    - type: euclidean_spearman
+      value: 79.90489748003296
+    - type: manhattan_pearson
+      value: 83.7540590666095
+    - type: manhattan_spearman
+      value: 79.86434577931573
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.92102564177941
+    - type: cos_sim_spearman
+      value: 84.98234585939103
+    - type: euclidean_pearson
+      value: 84.47729567593696
+    - type: euclidean_spearman
+      value: 85.09490696194469
+    - type: manhattan_pearson
+      value: 84.38622951588229
+    - type: manhattan_spearman
+      value: 85.02507171545574
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.1891164763377
+    - type: cos_sim_spearman
+      value: 80.7997969966883
+    - type: euclidean_pearson
+      value: 80.48572256162396
+    - type: euclidean_spearman
+      value: 80.57851903536378
+    - type: manhattan_pearson
+      value: 80.4324819433651
+    - type: manhattan_spearman
+      value: 80.5074526239062
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.64319975116025
+    - type: cos_sim_spearman
+      value: 84.88671197763652
+    - type: euclidean_pearson
+      value: 84.74692193293231
+    - type: euclidean_spearman
+      value: 85.27151722073653
+    - type: manhattan_pearson
+      value: 84.72460516785438
+    - type: manhattan_spearman
+      value: 85.26518899786687
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.24687565822381
+    - type: cos_sim_spearman
+      value: 85.60418454111263
+    - type: euclidean_pearson
+      value: 84.85829740169851
+    - type: euclidean_spearman
+      value: 85.66378014138306
+    - type: manhattan_pearson
+      value: 84.84672408808835
+    - type: manhattan_spearman
+      value: 85.63331924364891
+  - 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: 84.87758895415485
+    - type: cos_sim_spearman
+      value: 85.8193745617297
+    - type: euclidean_pearson
+      value: 85.78719118848134
+    - type: euclidean_spearman
+      value: 84.35797575385688
+    - type: manhattan_pearson
+      value: 85.97919844815692
+    - type: manhattan_spearman
+      value: 84.58334745175151
+  - 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: 67.27076035963599
+    - type: cos_sim_spearman
+      value: 67.21433656439973
+    - type: euclidean_pearson
+      value: 68.07434078679324
+    - type: euclidean_spearman
+      value: 66.0249731719049
+    - type: manhattan_pearson
+      value: 67.95495198947476
+    - type: manhattan_spearman
+      value: 65.99893908331886
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.22437747056817
+    - type: cos_sim_spearman
+      value: 85.0995685206174
+    - type: euclidean_pearson
+      value: 84.08616925603394
+    - type: euclidean_spearman
+      value: 84.89633925691658
+    - type: manhattan_pearson
+      value: 84.08332675923133
+    - type: manhattan_spearman
+      value: 84.8858228112915
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 87.6909022589666
+    - type: mrr
+      value: 96.43341952165481
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 57.660999999999994
+    - type: map_at_10
+      value: 67.625
+    - type: map_at_100
+      value: 68.07600000000001
+    - type: map_at_1000
+      value: 68.10199999999999
+    - type: map_at_3
+      value: 64.50399999999999
+    - type: map_at_5
+      value: 66.281
+    - type: mrr_at_1
+      value: 61.0
+    - type: mrr_at_10
+      value: 68.953
+    - type: mrr_at_100
+      value: 69.327
+    - type: mrr_at_1000
+      value: 69.352
+    - type: mrr_at_3
+      value: 66.833
+    - type: mrr_at_5
+      value: 68.05
+    - type: ndcg_at_1
+      value: 61.0
+    - type: ndcg_at_10
+      value: 72.369
+    - type: ndcg_at_100
+      value: 74.237
+    - type: ndcg_at_1000
+      value: 74.939
+    - type: ndcg_at_3
+      value: 67.284
+    - type: ndcg_at_5
+      value: 69.72500000000001
+    - type: precision_at_1
+      value: 61.0
+    - type: precision_at_10
+      value: 9.733
+    - type: precision_at_100
+      value: 1.0670000000000002
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 26.222
+    - type: precision_at_5
+      value: 17.4
+    - type: recall_at_1
+      value: 57.660999999999994
+    - type: recall_at_10
+      value: 85.656
+    - type: recall_at_100
+      value: 93.833
+    - type: recall_at_1000
+      value: 99.333
+    - type: recall_at_3
+      value: 71.961
+    - type: recall_at_5
+      value: 78.094
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.86930693069307
+    - type: cos_sim_ap
+      value: 96.76685487950894
+    - type: cos_sim_f1
+      value: 93.44587884806354
+    - type: cos_sim_precision
+      value: 92.80078895463511
+    - type: cos_sim_recall
+      value: 94.1
+    - type: dot_accuracy
+      value: 99.54356435643564
+    - type: dot_ap
+      value: 81.18659960405607
+    - type: dot_f1
+      value: 75.78008915304605
+    - type: dot_precision
+      value: 75.07360157016683
+    - type: dot_recall
+      value: 76.5
+    - type: euclidean_accuracy
+      value: 99.87326732673267
+    - type: euclidean_ap
+      value: 96.8102411908941
+    - type: euclidean_f1
+      value: 93.6127744510978
+    - type: euclidean_precision
+      value: 93.42629482071713
+    - type: euclidean_recall
+      value: 93.8
+    - type: manhattan_accuracy
+      value: 99.87425742574257
+    - type: manhattan_ap
+      value: 96.82857341435529
+    - type: manhattan_f1
+      value: 93.62129583124059
+    - type: manhattan_precision
+      value: 94.04641775983855
+    - type: manhattan_recall
+      value: 93.2
+    - type: max_accuracy
+      value: 99.87425742574257
+    - type: max_ap
+      value: 96.82857341435529
+    - type: max_f1
+      value: 93.62129583124059
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 65.92560972698926
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 34.92797240259008
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 55.244624045597654
+    - type: mrr
+      value: 56.185303666921314
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.02491987312937
+    - type: cos_sim_spearman
+      value: 32.055592206679734
+    - type: dot_pearson
+      value: 24.731627575422557
+    - type: dot_spearman
+      value: 24.308029077069733
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.231
+    - type: map_at_10
+      value: 1.899
+    - type: map_at_100
+      value: 9.498
+    - type: map_at_1000
+      value: 20.979999999999997
+    - type: map_at_3
+      value: 0.652
+    - type: map_at_5
+      value: 1.069
+    - type: mrr_at_1
+      value: 88.0
+    - type: mrr_at_10
+      value: 93.4
+    - type: mrr_at_100
+      value: 93.4
+    - type: mrr_at_1000
+      value: 93.4
+    - type: mrr_at_3
+      value: 93.0
+    - type: mrr_at_5
+      value: 93.4
+    - type: ndcg_at_1
+      value: 86.0
+    - type: ndcg_at_10
+      value: 75.375
+    - type: ndcg_at_100
+      value: 52.891999999999996
+    - type: ndcg_at_1000
+      value: 44.952999999999996
+    - type: ndcg_at_3
+      value: 81.05
+    - type: ndcg_at_5
+      value: 80.175
+    - type: precision_at_1
+      value: 88.0
+    - type: precision_at_10
+      value: 79.0
+    - type: precision_at_100
+      value: 53.16
+    - type: precision_at_1000
+      value: 19.408
+    - type: precision_at_3
+      value: 85.333
+    - type: precision_at_5
+      value: 84.0
+    - type: recall_at_1
+      value: 0.231
+    - type: recall_at_10
+      value: 2.078
+    - type: recall_at_100
+      value: 12.601
+    - type: recall_at_1000
+      value: 41.296
+    - type: recall_at_3
+      value: 0.6779999999999999
+    - type: recall_at_5
+      value: 1.1360000000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.782
+    - type: map_at_10
+      value: 10.204
+    - type: map_at_100
+      value: 16.176
+    - type: map_at_1000
+      value: 17.456
+    - type: map_at_3
+      value: 5.354
+    - type: map_at_5
+      value: 7.503
+    - type: mrr_at_1
+      value: 40.816
+    - type: mrr_at_10
+      value: 54.010000000000005
+    - type: mrr_at_100
+      value: 54.49
+    - type: mrr_at_1000
+      value: 54.49
+    - type: mrr_at_3
+      value: 48.980000000000004
+    - type: mrr_at_5
+      value: 51.735
+    - type: ndcg_at_1
+      value: 36.735
+    - type: ndcg_at_10
+      value: 26.61
+    - type: ndcg_at_100
+      value: 36.967
+    - type: ndcg_at_1000
+      value: 47.274
+    - type: ndcg_at_3
+      value: 30.363
+    - type: ndcg_at_5
+      value: 29.448999999999998
+    - type: precision_at_1
+      value: 40.816
+    - type: precision_at_10
+      value: 23.878
+    - type: precision_at_100
+      value: 7.693999999999999
+    - type: precision_at_1000
+      value: 1.4489999999999998
+    - type: precision_at_3
+      value: 31.293
+    - type: precision_at_5
+      value: 29.796
+    - type: recall_at_1
+      value: 2.782
+    - type: recall_at_10
+      value: 16.485
+    - type: recall_at_100
+      value: 46.924
+    - type: recall_at_1000
+      value: 79.365
+    - type: recall_at_3
+      value: 6.52
+    - type: recall_at_5
+      value: 10.48
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 70.35220000000001
+    - type: ap
+      value: 14.121012926379601
+    - type: f1
+      value: 54.22118125394254
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 59.90096208262592
+    - type: f1
+      value: 60.226896599794244
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 53.70780611078653
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.10734934732073
+    - type: cos_sim_ap
+      value: 77.58349999516054
+    - type: cos_sim_f1
+      value: 70.25391395868965
+    - type: cos_sim_precision
+      value: 70.06035161374967
+    - type: cos_sim_recall
+      value: 70.44854881266491
+    - type: dot_accuracy
+      value: 80.60439887941826
+    - type: dot_ap
+      value: 54.52935200483575
+    - type: dot_f1
+      value: 54.170444242973716
+    - type: dot_precision
+      value: 47.47715534366309
+    - type: dot_recall
+      value: 63.06068601583114
+    - type: euclidean_accuracy
+      value: 87.26828396018358
+    - type: euclidean_ap
+      value: 78.00158454104036
+    - type: euclidean_f1
+      value: 70.70292457670601
+    - type: euclidean_precision
+      value: 68.79680479281079
+    - type: euclidean_recall
+      value: 72.71767810026385
+    - type: manhattan_accuracy
+      value: 87.11330988853788
+    - type: manhattan_ap
+      value: 77.92527099601855
+    - type: manhattan_f1
+      value: 70.76488706365502
+    - type: manhattan_precision
+      value: 68.89055472263868
+    - type: manhattan_recall
+      value: 72.74406332453826
+    - type: max_accuracy
+      value: 87.26828396018358
+    - type: max_ap
+      value: 78.00158454104036
+    - type: max_f1
+      value: 70.76488706365502
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.80804905499282
+    - type: cos_sim_ap
+      value: 83.06187782630936
+    - type: cos_sim_f1
+      value: 74.99716435403985
+    - type: cos_sim_precision
+      value: 73.67951860931579
+    - type: cos_sim_recall
+      value: 76.36279642747151
+    - type: dot_accuracy
+      value: 81.83141227151008
+    - type: dot_ap
+      value: 67.18241090841795
+    - type: dot_f1
+      value: 62.216037571751606
+    - type: dot_precision
+      value: 56.749381227391005
+    - type: dot_recall
+      value: 68.84816753926701
+    - type: euclidean_accuracy
+      value: 87.91671517832887
+    - type: euclidean_ap
+      value: 83.56538942001427
+    - type: euclidean_f1
+      value: 75.7327253337256
+    - type: euclidean_precision
+      value: 72.48856036606828
+    - type: euclidean_recall
+      value: 79.28087465352634
+    - type: manhattan_accuracy
+      value: 87.86626304963713
+    - type: manhattan_ap
+      value: 83.52939841172832
+    - type: manhattan_f1
+      value: 75.73635656329888
+    - type: manhattan_precision
+      value: 72.99150182103836
+    - type: manhattan_recall
+      value: 78.69571912534647
+    - type: max_accuracy
+      value: 87.91671517832887
+    - type: max_ap
+      value: 83.56538942001427
+    - type: max_f1
+      value: 75.73635656329888
+---

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