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instructor-xl

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Eyalyoli commited on Dec 28, 2023

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use official model instead

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2_Dense/pytorch_model.bin +1 -1
README.md +2579 -16
config.json +1 -1
pytorch_model.bin +1 -1
tokenizer_config.json +1 -1

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 ---
 pipeline_tag: sentence-similarity
-language: en
-license: apache-2.0
 tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 - transformers
 ---
-# hku-nlp/instructor-xl
-This is a general embedding model: It maps **any** piece of text (e.g., a title, a sentence, a document, etc.) to a fixed-length vector in test time **without further training**. With instructions, the embeddings are **domain-specific** (e.g., specialized for science, finance, etc.) and **task-aware** (e.g., customized for classification, information retrieval, etc.)
-The model is easy to use with `sentence-transformer` library.
 ## Installation
 ```bash
-git clone https://github.com/HKUNLP/instructor-embedding
-cd sentence-transformers
-pip install -e .
 ```
 ## Compute your customized embeddings
 Then you can use the model like this to calculate domain-specific and task-aware embeddings:
 ```python
-from sentence_transformers import SentenceTransformer
 sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
-instruction = "Represent the Science title; Input:"
-model = SentenceTransformer('hku-nlp/instructor-xl')
-embeddings = model.encode([[instruction,sentence,0]])
 print(embeddings)
 ```
 ## Calculate Sentence similarities
 You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
 ```python
 from sklearn.metrics.pairwise import cosine_similarity
-sentences_a = [['Represent the Science sentence; Input: ','Parton energy loss in QCD matter',0],
-               ['Represent the Financial statement; Input: ','The Federal Reserve on Wednesday raised its benchmark interest rate.',0]
-sentences_b = [['Represent the Science sentence; Input: ','The Chiral Phase Transition in Dissipative Dynamics', 0],
-               ['Represent the Financial statement; Input: ','The funds rose less than 0.5 per cent on Friday',0]
 embeddings_a = model.encode(sentences_a)
 embeddings_b = model.encode(sentences_b)
 similarities = cosine_similarity(embeddings_a,embeddings_b)
 print(similarities)
 ```

 ---
 pipeline_tag: sentence-similarity
 tags:
+- text-embedding
+- embeddings
+- information-retrieval
+- beir
+- text-classification
+- language-model
+- text-clustering
+- text-semantic-similarity
+- text-evaluation
+- prompt-retrieval
+- text-reranking
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
 - transformers
+- t5
+- English
+- Sentence Similarity
+- natural_questions
+- ms_marco
+- fever
+- hotpot_qa
+- mteb
+language: en
+inference: false
+license: apache-2.0
+model-index:
+- name: final_xl_results
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 85.08955223880596
+    - type: ap
+      value: 52.66066378722476
+    - type: f1
+      value: 79.63340218960269
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 86.542
+    - type: ap
+      value: 81.92695193008987
+    - type: f1
+      value: 86.51466132573681
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 42.964
+    - type: f1
+      value: 41.43146249774862
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.872
+    - type: map_at_10
+      value: 46.342
+    - type: map_at_100
+      value: 47.152
+    - type: map_at_1000
+      value: 47.154
+    - type: map_at_3
+      value: 41.216
+    - type: map_at_5
+      value: 44.035999999999994
+    - type: mrr_at_1
+      value: 30.939
+    - type: mrr_at_10
+      value: 46.756
+    - type: mrr_at_100
+      value: 47.573
+    - type: mrr_at_1000
+      value: 47.575
+    - type: mrr_at_3
+      value: 41.548
+    - type: mrr_at_5
+      value: 44.425
+    - type: ndcg_at_1
+      value: 29.872
+    - type: ndcg_at_10
+      value: 55.65
+    - type: ndcg_at_100
+      value: 58.88099999999999
+    - type: ndcg_at_1000
+      value: 58.951
+    - type: ndcg_at_3
+      value: 45.0
+    - type: ndcg_at_5
+      value: 50.09
+    - type: precision_at_1
+      value: 29.872
+    - type: precision_at_10
+      value: 8.549
+    - type: precision_at_100
+      value: 0.991
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 18.658
+    - type: precision_at_5
+      value: 13.669999999999998
+    - type: recall_at_1
+      value: 29.872
+    - type: recall_at_10
+      value: 85.491
+    - type: recall_at_100
+      value: 99.075
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 55.974000000000004
+    - type: recall_at_5
+      value: 68.35
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 42.452729850641276
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 32.21141846480423
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 65.34710928952622
+    - type: mrr
+      value: 77.61124301983028
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_spearman
+      value: 84.15312230525639
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 82.66233766233766
+    - type: f1
+      value: 82.04175284777669
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 37.36697339826455
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 30.551241447593092
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 36.797000000000004
+    - type: map_at_10
+      value: 48.46
+    - type: map_at_100
+      value: 49.968
+    - type: map_at_1000
+      value: 50.080000000000005
+    - type: map_at_3
+      value: 44.71
+    - type: map_at_5
+      value: 46.592
+    - type: mrr_at_1
+      value: 45.494
+    - type: mrr_at_10
+      value: 54.747
+    - type: mrr_at_100
+      value: 55.43599999999999
+    - type: mrr_at_1000
+      value: 55.464999999999996
+    - type: mrr_at_3
+      value: 52.361000000000004
+    - type: mrr_at_5
+      value: 53.727000000000004
+    - type: ndcg_at_1
+      value: 45.494
+    - type: ndcg_at_10
+      value: 54.989
+    - type: ndcg_at_100
+      value: 60.096000000000004
+    - type: ndcg_at_1000
+      value: 61.58
+    - type: ndcg_at_3
+      value: 49.977
+    - type: ndcg_at_5
+      value: 51.964999999999996
+    - type: precision_at_1
+      value: 45.494
+    - type: precision_at_10
+      value: 10.558
+    - type: precision_at_100
+      value: 1.6049999999999998
+    - type: precision_at_1000
+      value: 0.203
+    - type: precision_at_3
+      value: 23.796
+    - type: precision_at_5
+      value: 16.881
+    - type: recall_at_1
+      value: 36.797000000000004
+    - type: recall_at_10
+      value: 66.83
+    - type: recall_at_100
+      value: 88.34100000000001
+    - type: recall_at_1000
+      value: 97.202
+    - type: recall_at_3
+      value: 51.961999999999996
+    - type: recall_at_5
+      value: 57.940000000000005
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.597
+    - type: map_at_10
+      value: 43.424
+    - type: map_at_100
+      value: 44.78
+    - type: map_at_1000
+      value: 44.913
+    - type: map_at_3
+      value: 40.315
+    - type: map_at_5
+      value: 41.987
+    - type: mrr_at_1
+      value: 40.382
+    - type: mrr_at_10
+      value: 49.219
+    - type: mrr_at_100
+      value: 49.895
+    - type: mrr_at_1000
+      value: 49.936
+    - type: mrr_at_3
+      value: 46.996
+    - type: mrr_at_5
+      value: 48.231
+    - type: ndcg_at_1
+      value: 40.382
+    - type: ndcg_at_10
+      value: 49.318
+    - type: ndcg_at_100
+      value: 53.839999999999996
+    - type: ndcg_at_1000
+      value: 55.82899999999999
+    - type: ndcg_at_3
+      value: 44.914
+    - type: ndcg_at_5
+      value: 46.798
+    - type: precision_at_1
+      value: 40.382
+    - type: precision_at_10
+      value: 9.274000000000001
+    - type: precision_at_100
+      value: 1.497
+    - type: precision_at_1000
+      value: 0.198
+    - type: precision_at_3
+      value: 21.592
+    - type: precision_at_5
+      value: 15.159
+    - type: recall_at_1
+      value: 32.597
+    - type: recall_at_10
+      value: 59.882000000000005
+    - type: recall_at_100
+      value: 78.446
+    - type: recall_at_1000
+      value: 90.88000000000001
+    - type: recall_at_3
+      value: 46.9
+    - type: recall_at_5
+      value: 52.222
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 43.8
+    - type: map_at_10
+      value: 57.293000000000006
+    - type: map_at_100
+      value: 58.321
+    - type: map_at_1000
+      value: 58.361
+    - type: map_at_3
+      value: 53.839999999999996
+    - type: map_at_5
+      value: 55.838
+    - type: mrr_at_1
+      value: 49.592000000000006
+    - type: mrr_at_10
+      value: 60.643
+    - type: mrr_at_100
+      value: 61.23499999999999
+    - type: mrr_at_1000
+      value: 61.251999999999995
+    - type: mrr_at_3
+      value: 58.265
+    - type: mrr_at_5
+      value: 59.717
+    - type: ndcg_at_1
+      value: 49.592000000000006
+    - type: ndcg_at_10
+      value: 63.364
+    - type: ndcg_at_100
+      value: 67.167
+    - type: ndcg_at_1000
+      value: 67.867
+    - type: ndcg_at_3
+      value: 57.912
+    - type: ndcg_at_5
+      value: 60.697
+    - type: precision_at_1
+      value: 49.592000000000006
+    - type: precision_at_10
+      value: 10.088
+    - type: precision_at_100
+      value: 1.2930000000000001
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 25.789
+    - type: precision_at_5
+      value: 17.541999999999998
+    - type: recall_at_1
+      value: 43.8
+    - type: recall_at_10
+      value: 77.635
+    - type: recall_at_100
+      value: 93.748
+    - type: recall_at_1000
+      value: 98.468
+    - type: recall_at_3
+      value: 63.223
+    - type: recall_at_5
+      value: 70.122
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.721
+    - type: map_at_10
+      value: 35.626999999999995
+    - type: map_at_100
+      value: 36.719
+    - type: map_at_1000
+      value: 36.8
+    - type: map_at_3
+      value: 32.781
+    - type: map_at_5
+      value: 34.333999999999996
+    - type: mrr_at_1
+      value: 29.604999999999997
+    - type: mrr_at_10
+      value: 37.564
+    - type: mrr_at_100
+      value: 38.505
+    - type: mrr_at_1000
+      value: 38.565
+    - type: mrr_at_3
+      value: 34.727000000000004
+    - type: mrr_at_5
+      value: 36.207
+    - type: ndcg_at_1
+      value: 29.604999999999997
+    - type: ndcg_at_10
+      value: 40.575
+    - type: ndcg_at_100
+      value: 45.613
+    - type: ndcg_at_1000
+      value: 47.676
+    - type: ndcg_at_3
+      value: 34.811
+    - type: ndcg_at_5
+      value: 37.491
+    - type: precision_at_1
+      value: 29.604999999999997
+    - type: precision_at_10
+      value: 6.1690000000000005
+    - type: precision_at_100
+      value: 0.906
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 14.237
+    - type: precision_at_5
+      value: 10.056
+    - type: recall_at_1
+      value: 27.721
+    - type: recall_at_10
+      value: 54.041
+    - type: recall_at_100
+      value: 76.62299999999999
+    - type: recall_at_1000
+      value: 92.134
+    - type: recall_at_3
+      value: 38.582
+    - type: recall_at_5
+      value: 44.989000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.553
+    - type: map_at_10
+      value: 25.384
+    - type: map_at_100
+      value: 26.655
+    - type: map_at_1000
+      value: 26.778000000000002
+    - type: map_at_3
+      value: 22.733
+    - type: map_at_5
+      value: 24.119
+    - type: mrr_at_1
+      value: 20.149
+    - type: mrr_at_10
+      value: 29.705
+    - type: mrr_at_100
+      value: 30.672
+    - type: mrr_at_1000
+      value: 30.737
+    - type: mrr_at_3
+      value: 27.032
+    - type: mrr_at_5
+      value: 28.369
+    - type: ndcg_at_1
+      value: 20.149
+    - type: ndcg_at_10
+      value: 30.843999999999998
+    - type: ndcg_at_100
+      value: 36.716
+    - type: ndcg_at_1000
+      value: 39.495000000000005
+    - type: ndcg_at_3
+      value: 25.918999999999997
+    - type: ndcg_at_5
+      value: 27.992
+    - type: precision_at_1
+      value: 20.149
+    - type: precision_at_10
+      value: 5.858
+    - type: precision_at_100
+      value: 1.009
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 12.645000000000001
+    - type: precision_at_5
+      value: 9.179
+    - type: recall_at_1
+      value: 16.553
+    - type: recall_at_10
+      value: 43.136
+    - type: recall_at_100
+      value: 68.562
+    - type: recall_at_1000
+      value: 88.208
+    - type: recall_at_3
+      value: 29.493000000000002
+    - type: recall_at_5
+      value: 34.751
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.000999999999998
+    - type: map_at_10
+      value: 39.004
+    - type: map_at_100
+      value: 40.461999999999996
+    - type: map_at_1000
+      value: 40.566
+    - type: map_at_3
+      value: 35.805
+    - type: map_at_5
+      value: 37.672
+    - type: mrr_at_1
+      value: 33.782000000000004
+    - type: mrr_at_10
+      value: 44.702
+    - type: mrr_at_100
+      value: 45.528
+    - type: mrr_at_1000
+      value: 45.576
+    - type: mrr_at_3
+      value: 42.14
+    - type: mrr_at_5
+      value: 43.651
+    - type: ndcg_at_1
+      value: 33.782000000000004
+    - type: ndcg_at_10
+      value: 45.275999999999996
+    - type: ndcg_at_100
+      value: 50.888
+    - type: ndcg_at_1000
+      value: 52.879
+    - type: ndcg_at_3
+      value: 40.191
+    - type: ndcg_at_5
+      value: 42.731
+    - type: precision_at_1
+      value: 33.782000000000004
+    - type: precision_at_10
+      value: 8.200000000000001
+    - type: precision_at_100
+      value: 1.287
+    - type: precision_at_1000
+      value: 0.16199999999999998
+    - type: precision_at_3
+      value: 19.185
+    - type: precision_at_5
+      value: 13.667000000000002
+    - type: recall_at_1
+      value: 28.000999999999998
+    - type: recall_at_10
+      value: 58.131
+    - type: recall_at_100
+      value: 80.869
+    - type: recall_at_1000
+      value: 93.931
+    - type: recall_at_3
+      value: 44.161
+    - type: recall_at_5
+      value: 50.592000000000006
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.047
+    - type: map_at_10
+      value: 38.596000000000004
+    - type: map_at_100
+      value: 40.116
+    - type: map_at_1000
+      value: 40.232
+    - type: map_at_3
+      value: 35.205
+    - type: map_at_5
+      value: 37.076
+    - type: mrr_at_1
+      value: 34.932
+    - type: mrr_at_10
+      value: 44.496
+    - type: mrr_at_100
+      value: 45.47
+    - type: mrr_at_1000
+      value: 45.519999999999996
+    - type: mrr_at_3
+      value: 41.743
+    - type: mrr_at_5
+      value: 43.352000000000004
+    - type: ndcg_at_1
+      value: 34.932
+    - type: ndcg_at_10
+      value: 44.901
+    - type: ndcg_at_100
+      value: 50.788999999999994
+    - type: ndcg_at_1000
+      value: 52.867
+    - type: ndcg_at_3
+      value: 39.449
+    - type: ndcg_at_5
+      value: 41.929
+    - type: precision_at_1
+      value: 34.932
+    - type: precision_at_10
+      value: 8.311
+    - type: precision_at_100
+      value: 1.3050000000000002
+    - type: precision_at_1000
+      value: 0.166
+    - type: precision_at_3
+      value: 18.836
+    - type: precision_at_5
+      value: 13.447000000000001
+    - type: recall_at_1
+      value: 28.047
+    - type: recall_at_10
+      value: 57.717
+    - type: recall_at_100
+      value: 82.182
+    - type: recall_at_1000
+      value: 95.82000000000001
+    - type: recall_at_3
+      value: 42.448
+    - type: recall_at_5
+      value: 49.071
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.861250000000005
+    - type: map_at_10
+      value: 37.529583333333335
+    - type: map_at_100
+      value: 38.7915
+    - type: map_at_1000
+      value: 38.90558333333335
+    - type: map_at_3
+      value: 34.57333333333333
+    - type: map_at_5
+      value: 36.187166666666656
+    - type: mrr_at_1
+      value: 32.88291666666666
+    - type: mrr_at_10
+      value: 41.79750000000001
+    - type: mrr_at_100
+      value: 42.63183333333333
+    - type: mrr_at_1000
+      value: 42.68483333333333
+    - type: mrr_at_3
+      value: 39.313750000000006
+    - type: mrr_at_5
+      value: 40.70483333333333
+    - type: ndcg_at_1
+      value: 32.88291666666666
+    - type: ndcg_at_10
+      value: 43.09408333333333
+    - type: ndcg_at_100
+      value: 48.22158333333333
+    - type: ndcg_at_1000
+      value: 50.358000000000004
+    - type: ndcg_at_3
+      value: 38.129583333333336
+    - type: ndcg_at_5
+      value: 40.39266666666666
+    - type: precision_at_1
+      value: 32.88291666666666
+    - type: precision_at_10
+      value: 7.5584999999999996
+    - type: precision_at_100
+      value: 1.1903333333333332
+    - type: precision_at_1000
+      value: 0.15658333333333332
+    - type: precision_at_3
+      value: 17.495916666666666
+    - type: precision_at_5
+      value: 12.373833333333332
+    - type: recall_at_1
+      value: 27.861250000000005
+    - type: recall_at_10
+      value: 55.215916666666665
+    - type: recall_at_100
+      value: 77.392
+    - type: recall_at_1000
+      value: 92.04908333333334
+    - type: recall_at_3
+      value: 41.37475
+    - type: recall_at_5
+      value: 47.22908333333333
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.064999999999998
+    - type: map_at_10
+      value: 31.635999999999996
+    - type: map_at_100
+      value: 32.596000000000004
+    - type: map_at_1000
+      value: 32.695
+    - type: map_at_3
+      value: 29.612
+    - type: map_at_5
+      value: 30.768
+    - type: mrr_at_1
+      value: 28.528
+    - type: mrr_at_10
+      value: 34.717
+    - type: mrr_at_100
+      value: 35.558
+    - type: mrr_at_1000
+      value: 35.626000000000005
+    - type: mrr_at_3
+      value: 32.745000000000005
+    - type: mrr_at_5
+      value: 33.819
+    - type: ndcg_at_1
+      value: 28.528
+    - type: ndcg_at_10
+      value: 35.647
+    - type: ndcg_at_100
+      value: 40.207
+    - type: ndcg_at_1000
+      value: 42.695
+    - type: ndcg_at_3
+      value: 31.878
+    - type: ndcg_at_5
+      value: 33.634
+    - type: precision_at_1
+      value: 28.528
+    - type: precision_at_10
+      value: 5.46
+    - type: precision_at_100
+      value: 0.84
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_3
+      value: 13.547999999999998
+    - type: precision_at_5
+      value: 9.325
+    - type: recall_at_1
+      value: 25.064999999999998
+    - type: recall_at_10
+      value: 45.096000000000004
+    - type: recall_at_100
+      value: 65.658
+    - type: recall_at_1000
+      value: 84.128
+    - type: recall_at_3
+      value: 34.337
+    - type: recall_at_5
+      value: 38.849000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.276
+    - type: map_at_10
+      value: 24.535
+    - type: map_at_100
+      value: 25.655
+    - type: map_at_1000
+      value: 25.782
+    - type: map_at_3
+      value: 22.228
+    - type: map_at_5
+      value: 23.612
+    - type: mrr_at_1
+      value: 21.266
+    - type: mrr_at_10
+      value: 28.474
+    - type: mrr_at_100
+      value: 29.398000000000003
+    - type: mrr_at_1000
+      value: 29.482000000000003
+    - type: mrr_at_3
+      value: 26.245
+    - type: mrr_at_5
+      value: 27.624
+    - type: ndcg_at_1
+      value: 21.266
+    - type: ndcg_at_10
+      value: 29.087000000000003
+    - type: ndcg_at_100
+      value: 34.374
+    - type: ndcg_at_1000
+      value: 37.433
+    - type: ndcg_at_3
+      value: 25.040000000000003
+    - type: ndcg_at_5
+      value: 27.116
+    - type: precision_at_1
+      value: 21.266
+    - type: precision_at_10
+      value: 5.258
+    - type: precision_at_100
+      value: 0.9299999999999999
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 11.849
+    - type: precision_at_5
+      value: 8.699
+    - type: recall_at_1
+      value: 17.276
+    - type: recall_at_10
+      value: 38.928000000000004
+    - type: recall_at_100
+      value: 62.529
+    - type: recall_at_1000
+      value: 84.44800000000001
+    - type: recall_at_3
+      value: 27.554000000000002
+    - type: recall_at_5
+      value: 32.915
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.297
+    - type: map_at_10
+      value: 36.957
+    - type: map_at_100
+      value: 38.252
+    - type: map_at_1000
+      value: 38.356
+    - type: map_at_3
+      value: 34.121
+    - type: map_at_5
+      value: 35.782000000000004
+    - type: mrr_at_1
+      value: 32.275999999999996
+    - type: mrr_at_10
+      value: 41.198
+    - type: mrr_at_100
+      value: 42.131
+    - type: mrr_at_1000
+      value: 42.186
+    - type: mrr_at_3
+      value: 38.557
+    - type: mrr_at_5
+      value: 40.12
+    - type: ndcg_at_1
+      value: 32.275999999999996
+    - type: ndcg_at_10
+      value: 42.516
+    - type: ndcg_at_100
+      value: 48.15
+    - type: ndcg_at_1000
+      value: 50.344
+    - type: ndcg_at_3
+      value: 37.423
+    - type: ndcg_at_5
+      value: 39.919
+    - type: precision_at_1
+      value: 32.275999999999996
+    - type: precision_at_10
+      value: 7.155
+    - type: precision_at_100
+      value: 1.123
+    - type: precision_at_1000
+      value: 0.14200000000000002
+    - type: precision_at_3
+      value: 17.163999999999998
+    - type: precision_at_5
+      value: 12.127
+    - type: recall_at_1
+      value: 27.297
+    - type: recall_at_10
+      value: 55.238
+    - type: recall_at_100
+      value: 79.2
+    - type: recall_at_1000
+      value: 94.258
+    - type: recall_at_3
+      value: 41.327000000000005
+    - type: recall_at_5
+      value: 47.588
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.142000000000003
+    - type: map_at_10
+      value: 38.769
+    - type: map_at_100
+      value: 40.292
+    - type: map_at_1000
+      value: 40.510000000000005
+    - type: map_at_3
+      value: 35.39
+    - type: map_at_5
+      value: 37.009
+    - type: mrr_at_1
+      value: 34.19
+    - type: mrr_at_10
+      value: 43.418
+    - type: mrr_at_100
+      value: 44.132
+    - type: mrr_at_1000
+      value: 44.175
+    - type: mrr_at_3
+      value: 40.547
+    - type: mrr_at_5
+      value: 42.088
+    - type: ndcg_at_1
+      value: 34.19
+    - type: ndcg_at_10
+      value: 45.14
+    - type: ndcg_at_100
+      value: 50.364
+    - type: ndcg_at_1000
+      value: 52.481
+    - type: ndcg_at_3
+      value: 39.466
+    - type: ndcg_at_5
+      value: 41.772
+    - type: precision_at_1
+      value: 34.19
+    - type: precision_at_10
+      value: 8.715
+    - type: precision_at_100
+      value: 1.6150000000000002
+    - type: precision_at_1000
+      value: 0.247
+    - type: precision_at_3
+      value: 18.248
+    - type: precision_at_5
+      value: 13.161999999999999
+    - type: recall_at_1
+      value: 29.142000000000003
+    - type: recall_at_10
+      value: 57.577999999999996
+    - type: recall_at_100
+      value: 81.428
+    - type: recall_at_1000
+      value: 94.017
+    - type: recall_at_3
+      value: 41.402
+    - type: recall_at_5
+      value: 47.695
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.039
+    - type: map_at_10
+      value: 30.669999999999998
+    - type: map_at_100
+      value: 31.682
+    - type: map_at_1000
+      value: 31.794
+    - type: map_at_3
+      value: 28.139999999999997
+    - type: map_at_5
+      value: 29.457
+    - type: mrr_at_1
+      value: 24.399
+    - type: mrr_at_10
+      value: 32.687
+    - type: mrr_at_100
+      value: 33.622
+    - type: mrr_at_1000
+      value: 33.698
+    - type: mrr_at_3
+      value: 30.407
+    - type: mrr_at_5
+      value: 31.552999999999997
+    - type: ndcg_at_1
+      value: 24.399
+    - type: ndcg_at_10
+      value: 35.472
+    - type: ndcg_at_100
+      value: 40.455000000000005
+    - type: ndcg_at_1000
+      value: 43.15
+    - type: ndcg_at_3
+      value: 30.575000000000003
+    - type: ndcg_at_5
+      value: 32.668
+    - type: precision_at_1
+      value: 24.399
+    - type: precision_at_10
+      value: 5.656
+    - type: precision_at_100
+      value: 0.874
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 13.062000000000001
+    - type: precision_at_5
+      value: 9.242
+    - type: recall_at_1
+      value: 22.039
+    - type: recall_at_10
+      value: 48.379
+    - type: recall_at_100
+      value: 71.11800000000001
+    - type: recall_at_1000
+      value: 91.095
+    - type: recall_at_3
+      value: 35.108
+    - type: recall_at_5
+      value: 40.015
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 10.144
+    - type: map_at_10
+      value: 18.238
+    - type: map_at_100
+      value: 20.143
+    - type: map_at_1000
+      value: 20.346
+    - type: map_at_3
+      value: 14.809
+    - type: map_at_5
+      value: 16.567999999999998
+    - type: mrr_at_1
+      value: 22.671
+    - type: mrr_at_10
+      value: 34.906
+    - type: mrr_at_100
+      value: 35.858000000000004
+    - type: mrr_at_1000
+      value: 35.898
+    - type: mrr_at_3
+      value: 31.238
+    - type: mrr_at_5
+      value: 33.342
+    - type: ndcg_at_1
+      value: 22.671
+    - type: ndcg_at_10
+      value: 26.540000000000003
+    - type: ndcg_at_100
+      value: 34.138000000000005
+    - type: ndcg_at_1000
+      value: 37.72
+    - type: ndcg_at_3
+      value: 20.766000000000002
+    - type: ndcg_at_5
+      value: 22.927
+    - type: precision_at_1
+      value: 22.671
+    - type: precision_at_10
+      value: 8.619
+    - type: precision_at_100
+      value: 1.678
+    - type: precision_at_1000
+      value: 0.23500000000000001
+    - type: precision_at_3
+      value: 15.592
+    - type: precision_at_5
+      value: 12.43
+    - type: recall_at_1
+      value: 10.144
+    - type: recall_at_10
+      value: 33.46
+    - type: recall_at_100
+      value: 59.758
+    - type: recall_at_1000
+      value: 79.704
+    - type: recall_at_3
+      value: 19.604
+    - type: recall_at_5
+      value: 25.367
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.654
+    - type: map_at_10
+      value: 18.506
+    - type: map_at_100
+      value: 26.412999999999997
+    - type: map_at_1000
+      value: 28.13
+    - type: map_at_3
+      value: 13.379
+    - type: map_at_5
+      value: 15.529000000000002
+    - type: mrr_at_1
+      value: 66.0
+    - type: mrr_at_10
+      value: 74.13
+    - type: mrr_at_100
+      value: 74.48700000000001
+    - type: mrr_at_1000
+      value: 74.49799999999999
+    - type: mrr_at_3
+      value: 72.75
+    - type: mrr_at_5
+      value: 73.762
+    - type: ndcg_at_1
+      value: 54.50000000000001
+    - type: ndcg_at_10
+      value: 40.236
+    - type: ndcg_at_100
+      value: 44.690999999999995
+    - type: ndcg_at_1000
+      value: 52.195
+    - type: ndcg_at_3
+      value: 45.632
+    - type: ndcg_at_5
+      value: 42.952
+    - type: precision_at_1
+      value: 66.0
+    - type: precision_at_10
+      value: 31.724999999999998
+    - type: precision_at_100
+      value: 10.299999999999999
+    - type: precision_at_1000
+      value: 2.194
+    - type: precision_at_3
+      value: 48.75
+    - type: precision_at_5
+      value: 41.6
+    - type: recall_at_1
+      value: 8.654
+    - type: recall_at_10
+      value: 23.74
+    - type: recall_at_100
+      value: 50.346999999999994
+    - type: recall_at_1000
+      value: 74.376
+    - type: recall_at_3
+      value: 14.636
+    - type: recall_at_5
+      value: 18.009
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 53.245
+    - type: f1
+      value: 48.74520523753552
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 51.729
+    - type: map_at_10
+      value: 63.904
+    - type: map_at_100
+      value: 64.363
+    - type: map_at_1000
+      value: 64.38199999999999
+    - type: map_at_3
+      value: 61.393
+    - type: map_at_5
+      value: 63.02100000000001
+    - type: mrr_at_1
+      value: 55.686
+    - type: mrr_at_10
+      value: 67.804
+    - type: mrr_at_100
+      value: 68.15299999999999
+    - type: mrr_at_1000
+      value: 68.161
+    - type: mrr_at_3
+      value: 65.494
+    - type: mrr_at_5
+      value: 67.01599999999999
+    - type: ndcg_at_1
+      value: 55.686
+    - type: ndcg_at_10
+      value: 70.025
+    - type: ndcg_at_100
+      value: 72.011
+    - type: ndcg_at_1000
+      value: 72.443
+    - type: ndcg_at_3
+      value: 65.32900000000001
+    - type: ndcg_at_5
+      value: 68.05600000000001
+    - type: precision_at_1
+      value: 55.686
+    - type: precision_at_10
+      value: 9.358
+    - type: precision_at_100
+      value: 1.05
+    - type: precision_at_1000
+      value: 0.11
+    - type: precision_at_3
+      value: 26.318
+    - type: precision_at_5
+      value: 17.321
+    - type: recall_at_1
+      value: 51.729
+    - type: recall_at_10
+      value: 85.04
+    - type: recall_at_100
+      value: 93.777
+    - type: recall_at_1000
+      value: 96.824
+    - type: recall_at_3
+      value: 72.521
+    - type: recall_at_5
+      value: 79.148
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.765
+    - type: map_at_10
+      value: 39.114
+    - type: map_at_100
+      value: 40.987
+    - type: map_at_1000
+      value: 41.155
+    - type: map_at_3
+      value: 34.028000000000006
+    - type: map_at_5
+      value: 36.925000000000004
+    - type: mrr_at_1
+      value: 46.451
+    - type: mrr_at_10
+      value: 54.711
+    - type: mrr_at_100
+      value: 55.509
+    - type: mrr_at_1000
+      value: 55.535000000000004
+    - type: mrr_at_3
+      value: 52.649
+    - type: mrr_at_5
+      value: 53.729000000000006
+    - type: ndcg_at_1
+      value: 46.451
+    - type: ndcg_at_10
+      value: 46.955999999999996
+    - type: ndcg_at_100
+      value: 53.686
+    - type: ndcg_at_1000
+      value: 56.230000000000004
+    - type: ndcg_at_3
+      value: 43.374
+    - type: ndcg_at_5
+      value: 44.372
+    - type: precision_at_1
+      value: 46.451
+    - type: precision_at_10
+      value: 13.256
+    - type: precision_at_100
+      value: 2.019
+    - type: precision_at_1000
+      value: 0.247
+    - type: precision_at_3
+      value: 29.115000000000002
+    - type: precision_at_5
+      value: 21.389
+    - type: recall_at_1
+      value: 23.765
+    - type: recall_at_10
+      value: 53.452999999999996
+    - type: recall_at_100
+      value: 78.828
+    - type: recall_at_1000
+      value: 93.938
+    - type: recall_at_3
+      value: 39.023
+    - type: recall_at_5
+      value: 45.18
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.918000000000003
+    - type: map_at_10
+      value: 46.741
+    - type: map_at_100
+      value: 47.762
+    - type: map_at_1000
+      value: 47.849000000000004
+    - type: map_at_3
+      value: 43.578
+    - type: map_at_5
+      value: 45.395
+    - type: mrr_at_1
+      value: 63.834999999999994
+    - type: mrr_at_10
+      value: 71.312
+    - type: mrr_at_100
+      value: 71.695
+    - type: mrr_at_1000
+      value: 71.714
+    - type: mrr_at_3
+      value: 69.82000000000001
+    - type: mrr_at_5
+      value: 70.726
+    - type: ndcg_at_1
+      value: 63.834999999999994
+    - type: ndcg_at_10
+      value: 55.879999999999995
+    - type: ndcg_at_100
+      value: 59.723000000000006
+    - type: ndcg_at_1000
+      value: 61.49400000000001
+    - type: ndcg_at_3
+      value: 50.964
+    - type: ndcg_at_5
+      value: 53.47
+    - type: precision_at_1
+      value: 63.834999999999994
+    - type: precision_at_10
+      value: 11.845
+    - type: precision_at_100
+      value: 1.4869999999999999
+    - type: precision_at_1000
+      value: 0.172
+    - type: precision_at_3
+      value: 32.158
+    - type: precision_at_5
+      value: 21.278
+    - type: recall_at_1
+      value: 31.918000000000003
+    - type: recall_at_10
+      value: 59.223000000000006
+    - type: recall_at_100
+      value: 74.328
+    - type: recall_at_1000
+      value: 86.05000000000001
+    - type: recall_at_3
+      value: 48.238
+    - type: recall_at_5
+      value: 53.193999999999996
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 79.7896
+    - type: ap
+      value: 73.65166029460288
+    - type: f1
+      value: 79.71794693711813
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.239
+    - type: map_at_10
+      value: 34.542
+    - type: map_at_100
+      value: 35.717999999999996
+    - type: map_at_1000
+      value: 35.764
+    - type: map_at_3
+      value: 30.432
+    - type: map_at_5
+      value: 32.81
+    - type: mrr_at_1
+      value: 22.908
+    - type: mrr_at_10
+      value: 35.127
+    - type: mrr_at_100
+      value: 36.238
+    - type: mrr_at_1000
+      value: 36.278
+    - type: mrr_at_3
+      value: 31.076999999999998
+    - type: mrr_at_5
+      value: 33.419
+    - type: ndcg_at_1
+      value: 22.908
+    - type: ndcg_at_10
+      value: 41.607
+    - type: ndcg_at_100
+      value: 47.28
+    - type: ndcg_at_1000
+      value: 48.414
+    - type: ndcg_at_3
+      value: 33.253
+    - type: ndcg_at_5
+      value: 37.486000000000004
+    - type: precision_at_1
+      value: 22.908
+    - type: precision_at_10
+      value: 6.645
+    - type: precision_at_100
+      value: 0.9490000000000001
+    - type: precision_at_1000
+      value: 0.105
+    - type: precision_at_3
+      value: 14.130999999999998
+    - type: precision_at_5
+      value: 10.616
+    - type: recall_at_1
+      value: 22.239
+    - type: recall_at_10
+      value: 63.42
+    - type: recall_at_100
+      value: 89.696
+    - type: recall_at_1000
+      value: 98.351
+    - type: recall_at_3
+      value: 40.77
+    - type: recall_at_5
+      value: 50.93
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 95.06839945280439
+    - type: f1
+      value: 94.74276398224072
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 72.25718194254446
+    - type: f1
+      value: 53.91164489161391
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 71.47948890383323
+    - type: f1
+      value: 69.98520247230257
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 76.46603900470748
+    - type: f1
+      value: 76.44111526065399
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 33.19106070798198
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 30.78772205248094
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.811231631488507
+    - type: mrr
+      value: 32.98200485378021
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.9
+    - type: map_at_10
+      value: 13.703000000000001
+    - type: map_at_100
+      value: 17.251
+    - type: map_at_1000
+      value: 18.795
+    - type: map_at_3
+      value: 10.366999999999999
+    - type: map_at_5
+      value: 11.675
+    - type: mrr_at_1
+      value: 47.059
+    - type: mrr_at_10
+      value: 55.816
+    - type: mrr_at_100
+      value: 56.434
+    - type: mrr_at_1000
+      value: 56.467
+    - type: mrr_at_3
+      value: 53.973000000000006
+    - type: mrr_at_5
+      value: 55.257999999999996
+    - type: ndcg_at_1
+      value: 44.737
+    - type: ndcg_at_10
+      value: 35.997
+    - type: ndcg_at_100
+      value: 33.487
+    - type: ndcg_at_1000
+      value: 41.897
+    - type: ndcg_at_3
+      value: 41.18
+    - type: ndcg_at_5
+      value: 38.721
+    - type: precision_at_1
+      value: 46.129999999999995
+    - type: precision_at_10
+      value: 26.533
+    - type: precision_at_100
+      value: 8.706
+    - type: precision_at_1000
+      value: 2.16
+    - type: precision_at_3
+      value: 38.493
+    - type: precision_at_5
+      value: 33.189
+    - type: recall_at_1
+      value: 6.9
+    - type: recall_at_10
+      value: 17.488999999999997
+    - type: recall_at_100
+      value: 34.583000000000006
+    - type: recall_at_1000
+      value: 64.942
+    - type: recall_at_3
+      value: 11.494
+    - type: recall_at_5
+      value: 13.496
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 33.028999999999996
+    - type: map_at_10
+      value: 49.307
+    - type: map_at_100
+      value: 50.205
+    - type: map_at_1000
+      value: 50.23
+    - type: map_at_3
+      value: 44.782
+    - type: map_at_5
+      value: 47.599999999999994
+    - type: mrr_at_1
+      value: 37.108999999999995
+    - type: mrr_at_10
+      value: 51.742999999999995
+    - type: mrr_at_100
+      value: 52.405
+    - type: mrr_at_1000
+      value: 52.422000000000004
+    - type: mrr_at_3
+      value: 48.087999999999994
+    - type: mrr_at_5
+      value: 50.414
+    - type: ndcg_at_1
+      value: 37.08
+    - type: ndcg_at_10
+      value: 57.236
+    - type: ndcg_at_100
+      value: 60.931999999999995
+    - type: ndcg_at_1000
+      value: 61.522
+    - type: ndcg_at_3
+      value: 48.93
+    - type: ndcg_at_5
+      value: 53.561
+    - type: precision_at_1
+      value: 37.08
+    - type: precision_at_10
+      value: 9.386
+    - type: precision_at_100
+      value: 1.1480000000000001
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 22.258
+    - type: precision_at_5
+      value: 16.025
+    - type: recall_at_1
+      value: 33.028999999999996
+    - type: recall_at_10
+      value: 78.805
+    - type: recall_at_100
+      value: 94.643
+    - type: recall_at_1000
+      value: 99.039
+    - type: recall_at_3
+      value: 57.602
+    - type: recall_at_5
+      value: 68.253
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 71.122
+    - type: map_at_10
+      value: 85.237
+    - type: map_at_100
+      value: 85.872
+    - type: map_at_1000
+      value: 85.885
+    - type: map_at_3
+      value: 82.27499999999999
+    - type: map_at_5
+      value: 84.13199999999999
+    - type: mrr_at_1
+      value: 81.73
+    - type: mrr_at_10
+      value: 87.834
+    - type: mrr_at_100
+      value: 87.92
+    - type: mrr_at_1000
+      value: 87.921
+    - type: mrr_at_3
+      value: 86.878
+    - type: mrr_at_5
+      value: 87.512
+    - type: ndcg_at_1
+      value: 81.73
+    - type: ndcg_at_10
+      value: 88.85499999999999
+    - type: ndcg_at_100
+      value: 89.992
+    - type: ndcg_at_1000
+      value: 90.07
+    - type: ndcg_at_3
+      value: 85.997
+    - type: ndcg_at_5
+      value: 87.55199999999999
+    - type: precision_at_1
+      value: 81.73
+    - type: precision_at_10
+      value: 13.491
+    - type: precision_at_100
+      value: 1.536
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.623
+    - type: precision_at_5
+      value: 24.742
+    - type: recall_at_1
+      value: 71.122
+    - type: recall_at_10
+      value: 95.935
+    - type: recall_at_100
+      value: 99.657
+    - type: recall_at_1000
+      value: 99.996
+    - type: recall_at_3
+      value: 87.80799999999999
+    - type: recall_at_5
+      value: 92.161
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 63.490029238193756
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 65.13153408508836
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.202999999999999
+    - type: map_at_10
+      value: 10.174
+    - type: map_at_100
+      value: 12.138
+    - type: map_at_1000
+      value: 12.418
+    - type: map_at_3
+      value: 7.379
+    - type: map_at_5
+      value: 8.727
+    - type: mrr_at_1
+      value: 20.7
+    - type: mrr_at_10
+      value: 30.389
+    - type: mrr_at_100
+      value: 31.566
+    - type: mrr_at_1000
+      value: 31.637999999999998
+    - type: mrr_at_3
+      value: 27.133000000000003
+    - type: mrr_at_5
+      value: 29.078
+    - type: ndcg_at_1
+      value: 20.7
+    - type: ndcg_at_10
+      value: 17.355999999999998
+    - type: ndcg_at_100
+      value: 25.151
+    - type: ndcg_at_1000
+      value: 30.37
+    - type: ndcg_at_3
+      value: 16.528000000000002
+    - type: ndcg_at_5
+      value: 14.396999999999998
+    - type: precision_at_1
+      value: 20.7
+    - type: precision_at_10
+      value: 8.98
+    - type: precision_at_100
+      value: 2.015
+    - type: precision_at_1000
+      value: 0.327
+    - type: precision_at_3
+      value: 15.367
+    - type: precision_at_5
+      value: 12.559999999999999
+    - type: recall_at_1
+      value: 4.202999999999999
+    - type: recall_at_10
+      value: 18.197
+    - type: recall_at_100
+      value: 40.903
+    - type: recall_at_1000
+      value: 66.427
+    - type: recall_at_3
+      value: 9.362
+    - type: recall_at_5
+      value: 12.747
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_spearman
+      value: 81.69890989765257
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_spearman
+      value: 75.31953790551489
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_spearman
+      value: 87.44050861280759
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_spearman
+      value: 81.86922869270393
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_spearman
+      value: 88.9399170304284
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_spearman
+      value: 85.38015314088582
+  - 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_spearman
+      value: 90.53653527788835
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_spearman
+      value: 68.64526474250209
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_spearman
+      value: 86.56156983963042
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 79.48610254648003
+    - type: mrr
+      value: 94.02481505422682
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 48.983
+    - type: map_at_10
+      value: 59.077999999999996
+    - type: map_at_100
+      value: 59.536
+    - type: map_at_1000
+      value: 59.575
+    - type: map_at_3
+      value: 55.691
+    - type: map_at_5
+      value: 57.410000000000004
+    - type: mrr_at_1
+      value: 51.666999999999994
+    - type: mrr_at_10
+      value: 60.427
+    - type: mrr_at_100
+      value: 60.763
+    - type: mrr_at_1000
+      value: 60.79900000000001
+    - type: mrr_at_3
+      value: 57.556
+    - type: mrr_at_5
+      value: 59.089000000000006
+    - type: ndcg_at_1
+      value: 51.666999999999994
+    - type: ndcg_at_10
+      value: 64.559
+    - type: ndcg_at_100
+      value: 66.58
+    - type: ndcg_at_1000
+      value: 67.64
+    - type: ndcg_at_3
+      value: 58.287
+    - type: ndcg_at_5
+      value: 61.001000000000005
+    - type: precision_at_1
+      value: 51.666999999999994
+    - type: precision_at_10
+      value: 9.067
+    - type: precision_at_100
+      value: 1.0170000000000001
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 23.0
+    - type: precision_at_5
+      value: 15.6
+    - type: recall_at_1
+      value: 48.983
+    - type: recall_at_10
+      value: 80.289
+    - type: recall_at_100
+      value: 89.43299999999999
+    - type: recall_at_1000
+      value: 97.667
+    - type: recall_at_3
+      value: 62.978
+    - type: recall_at_5
+      value: 69.872
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.79009900990098
+    - type: cos_sim_ap
+      value: 94.94115052608419
+    - type: cos_sim_f1
+      value: 89.1260162601626
+    - type: cos_sim_precision
+      value: 90.599173553719
+    - type: cos_sim_recall
+      value: 87.7
+    - type: dot_accuracy
+      value: 99.79009900990098
+    - type: dot_ap
+      value: 94.94115052608419
+    - type: dot_f1
+      value: 89.1260162601626
+    - type: dot_precision
+      value: 90.599173553719
+    - type: dot_recall
+      value: 87.7
+    - type: euclidean_accuracy
+      value: 99.79009900990098
+    - type: euclidean_ap
+      value: 94.94115052608419
+    - type: euclidean_f1
+      value: 89.1260162601626
+    - type: euclidean_precision
+      value: 90.599173553719
+    - type: euclidean_recall
+      value: 87.7
+    - type: manhattan_accuracy
+      value: 99.7940594059406
+    - type: manhattan_ap
+      value: 94.95271414642431
+    - type: manhattan_f1
+      value: 89.24508790072387
+    - type: manhattan_precision
+      value: 92.3982869379015
+    - type: manhattan_recall
+      value: 86.3
+    - type: max_accuracy
+      value: 99.7940594059406
+    - type: max_ap
+      value: 94.95271414642431
+    - type: max_f1
+      value: 89.24508790072387
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 68.43866571935851
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 35.16579026551532
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 52.518952473513934
+    - type: mrr
+      value: 53.292457134368895
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.12529588316604
+    - type: cos_sim_spearman
+      value: 32.31662126895294
+    - type: dot_pearson
+      value: 31.125303796647056
+    - type: dot_spearman
+      value: 32.31662126895294
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.219
+    - type: map_at_10
+      value: 1.7469999999999999
+    - type: map_at_100
+      value: 10.177999999999999
+    - type: map_at_1000
+      value: 26.108999999999998
+    - type: map_at_3
+      value: 0.64
+    - type: map_at_5
+      value: 0.968
+    - type: mrr_at_1
+      value: 82.0
+    - type: mrr_at_10
+      value: 89.067
+    - type: mrr_at_100
+      value: 89.067
+    - type: mrr_at_1000
+      value: 89.067
+    - type: mrr_at_3
+      value: 88.333
+    - type: mrr_at_5
+      value: 88.73299999999999
+    - type: ndcg_at_1
+      value: 78.0
+    - type: ndcg_at_10
+      value: 71.398
+    - type: ndcg_at_100
+      value: 55.574999999999996
+    - type: ndcg_at_1000
+      value: 51.771
+    - type: ndcg_at_3
+      value: 77.765
+    - type: ndcg_at_5
+      value: 73.614
+    - type: precision_at_1
+      value: 82.0
+    - type: precision_at_10
+      value: 75.4
+    - type: precision_at_100
+      value: 58.040000000000006
+    - type: precision_at_1000
+      value: 23.516000000000002
+    - type: precision_at_3
+      value: 84.0
+    - type: precision_at_5
+      value: 78.4
+    - type: recall_at_1
+      value: 0.219
+    - type: recall_at_10
+      value: 1.958
+    - type: recall_at_100
+      value: 13.797999999999998
+    - type: recall_at_1000
+      value: 49.881
+    - type: recall_at_3
+      value: 0.672
+    - type: recall_at_5
+      value: 1.0370000000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 1.8610000000000002
+    - type: map_at_10
+      value: 8.705
+    - type: map_at_100
+      value: 15.164
+    - type: map_at_1000
+      value: 16.78
+    - type: map_at_3
+      value: 4.346
+    - type: map_at_5
+      value: 6.151
+    - type: mrr_at_1
+      value: 22.448999999999998
+    - type: mrr_at_10
+      value: 41.556
+    - type: mrr_at_100
+      value: 42.484
+    - type: mrr_at_1000
+      value: 42.494
+    - type: mrr_at_3
+      value: 37.755
+    - type: mrr_at_5
+      value: 40.102
+    - type: ndcg_at_1
+      value: 21.429000000000002
+    - type: ndcg_at_10
+      value: 23.439
+    - type: ndcg_at_100
+      value: 36.948
+    - type: ndcg_at_1000
+      value: 48.408
+    - type: ndcg_at_3
+      value: 22.261
+    - type: ndcg_at_5
+      value: 23.085
+    - type: precision_at_1
+      value: 22.448999999999998
+    - type: precision_at_10
+      value: 21.633
+    - type: precision_at_100
+      value: 8.02
+    - type: precision_at_1000
+      value: 1.5939999999999999
+    - type: precision_at_3
+      value: 23.810000000000002
+    - type: precision_at_5
+      value: 24.490000000000002
+    - type: recall_at_1
+      value: 1.8610000000000002
+    - type: recall_at_10
+      value: 15.876000000000001
+    - type: recall_at_100
+      value: 50.300999999999995
+    - type: recall_at_1000
+      value: 86.098
+    - type: recall_at_3
+      value: 5.892
+    - type: recall_at_5
+      value: 9.443
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 70.3264
+    - type: ap
+      value: 13.249577616243794
+    - type: f1
+      value: 53.621518367695685
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 61.57611771363894
+    - type: f1
+      value: 61.79797478568639
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 53.38315344479284
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.55438993860642
+    - type: cos_sim_ap
+      value: 77.98702600017738
+    - type: cos_sim_f1
+      value: 71.94971653931476
+    - type: cos_sim_precision
+      value: 67.50693802035153
+    - type: cos_sim_recall
+      value: 77.01846965699208
+    - type: dot_accuracy
+      value: 87.55438993860642
+    - type: dot_ap
+      value: 77.98702925907986
+    - type: dot_f1
+      value: 71.94971653931476
+    - type: dot_precision
+      value: 67.50693802035153
+    - type: dot_recall
+      value: 77.01846965699208
+    - type: euclidean_accuracy
+      value: 87.55438993860642
+    - type: euclidean_ap
+      value: 77.98702951957925
+    - type: euclidean_f1
+      value: 71.94971653931476
+    - type: euclidean_precision
+      value: 67.50693802035153
+    - type: euclidean_recall
+      value: 77.01846965699208
+    - type: manhattan_accuracy
+      value: 87.54246885617214
+    - type: manhattan_ap
+      value: 77.95531413902947
+    - type: manhattan_f1
+      value: 71.93605683836589
+    - type: manhattan_precision
+      value: 69.28152492668622
+    - type: manhattan_recall
+      value: 74.80211081794195
+    - type: max_accuracy
+      value: 87.55438993860642
+    - type: max_ap
+      value: 77.98702951957925
+    - type: max_f1
+      value: 71.94971653931476
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.47296930182016
+    - type: cos_sim_ap
+      value: 86.92853616302108
+    - type: cos_sim_f1
+      value: 79.35138351681047
+    - type: cos_sim_precision
+      value: 76.74820143884892
+    - type: cos_sim_recall
+      value: 82.13735756082538
+    - type: dot_accuracy
+      value: 89.47296930182016
+    - type: dot_ap
+      value: 86.92854339601595
+    - type: dot_f1
+      value: 79.35138351681047
+    - type: dot_precision
+      value: 76.74820143884892
+    - type: dot_recall
+      value: 82.13735756082538
+    - type: euclidean_accuracy
+      value: 89.47296930182016
+    - type: euclidean_ap
+      value: 86.92854191061649
+    - type: euclidean_f1
+      value: 79.35138351681047
+    - type: euclidean_precision
+      value: 76.74820143884892
+    - type: euclidean_recall
+      value: 82.13735756082538
+    - type: manhattan_accuracy
+      value: 89.47685023479644
+    - type: manhattan_ap
+      value: 86.90063722679578
+    - type: manhattan_f1
+      value: 79.30753865502702
+    - type: manhattan_precision
+      value: 76.32066068631639
+    - type: manhattan_recall
+      value: 82.53772713273791
+    - type: max_accuracy
+      value: 89.47685023479644
+    - type: max_ap
+      value: 86.92854339601595
+    - type: max_f1
+      value: 79.35138351681047
 ---
+# hkunlp/instructor-xl
+We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks!
+The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out [our paper](https://arxiv.org/abs/2212.09741) and [project page](https://instructor-embedding.github.io/)!
+**************************** **Updates** ****************************
+* 01/21: We released a new [checkpoint](https://huggingface.co/hkunlp/instructor-xl) trained with hard negatives, which gives better performance.
+* 12/21: We released our [paper](https://arxiv.org/abs/2212.09741), [code](https://github.com/HKUNLP/instructor-embedding), [checkpoint](https://huggingface.co/hkunlp/instructor-xl) and [project page](https://instructor-embedding.github.io/)! Check them out!
+## Quick start
+<hr />
 ## Installation
 ```bash
+pip install InstructorEmbedding
 ```
 ## Compute your customized embeddings
 Then you can use the model like this to calculate domain-specific and task-aware embeddings:
 ```python
+from InstructorEmbedding import INSTRUCTOR
+model = INSTRUCTOR('hkunlp/instructor-xl')
 sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
+instruction = "Represent the Science title:"
+embeddings = model.encode([[instruction,sentence]])
 print(embeddings)
 ```
+## Use cases
+<hr />
+## Calculate embeddings for your customized texts
+If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Represent the `domain` `text_type` for `task_objective`:
+* `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc.
+* `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc.
+* `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.
 ## Calculate Sentence similarities
 You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**.
 ```python
 from sklearn.metrics.pairwise import cosine_similarity
+sentences_a = [['Represent the Science sentence: ','Parton energy loss in QCD matter'],
+               ['Represent the Financial statement: ','The Federal Reserve on Wednesday raised its benchmark interest rate.']]
+sentences_b = [['Represent the Science sentence: ','The Chiral Phase Transition in Dissipative Dynamics'],
+               ['Represent the Financial statement: ','The funds rose less than 0.5 per cent on Friday']]
 embeddings_a = model.encode(sentences_a)
 embeddings_b = model.encode(sentences_b)
 similarities = cosine_similarity(embeddings_a,embeddings_b)
 print(similarities)
+```
+## Information Retrieval
+You can also use **customized embeddings** for information retrieval.
+```python
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+query  = [['Represent the Wikipedia question for retrieving supporting documents: ','where is the food stored in a yam plant']]
+corpus = [['Represent the Wikipedia document for retrieval: ','Capitalism has been dominant in the Western world since the end of feudalism, but most feel[who?] that the term "mixed economies" more precisely describes most contemporary economies, due to their containing both private-owned and state-owned enterprises. In capitalism, prices determine the demand-supply scale. For example, higher demand for certain goods and services lead to higher prices and lower demand for certain goods lead to lower prices.'],
+          ['Represent the Wikipedia document for retrieval: ',"The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loansâ€”and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession"],
+          ['Represent the Wikipedia document for retrieval: ','Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.']]
+query_embeddings = model.encode(query)
+corpus_embeddings = model.encode(corpus)
+similarities = cosine_similarity(query_embeddings,corpus_embeddings)
+retrieved_doc_id = np.argmax(similarities)
+print(retrieved_doc_id)
+```
+## Clustering
+Use **customized embeddings** for clustering texts in groups.
+```python
+import sklearn.cluster
+sentences = [['Represent the Medicine sentence for clustering: ','Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity'],
+             ['Represent the Medicine sentence for clustering: ','Comparison of Atmospheric Neutrino Flux Calculations at Low Energies'],
+             ['Represent the Medicine sentence for clustering: ','Fermion Bags in the Massive Gross-Neveu Model'],
+             ['Represent the Medicine sentence for clustering: ',"QCD corrections to Associated t-tbar-H production at the Tevatron"],
+             ['Represent the Medicine sentence for clustering: ','A New Analysis of the R Measurements: Resonance Parameters of the Higher,  Vector States of Charmonium']]
+embeddings = model.encode(sentences)
+clustering_model = sklearn.cluster.MiniBatchKMeans(n_clusters=2)
+clustering_model.fit(embeddings)
+cluster_assignment = clustering_model.labels_
+print(cluster_assignment)
 ```

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