README.md

metadata

base_model: baai/bge-base-en-v1.5
language:
  - en
library_name: model2vec
license: mit
model_name: brown-beetle-base-v1.1
tags:
  - embeddings
  - static-embeddings
  - sentence-transformers
  - mteb
model-index:
  - name: brown-beetle-base-v1.1
    results:
      - dataset:
          config: en-ext
          name: MTEB AmazonCounterfactualClassification (en-ext)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 66.8815592203898
          - type: ap
            value: 18.195189738998792
          - type: ap_weighted
            value: 18.195189738998792
          - type: f1
            value: 55.16513139500548
          - type: f1_weighted
            value: 73.16351793408617
          - type: main_score
            value: 66.8815592203898
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonCounterfactualClassification (en)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 70.19402985074626
          - type: ap
            value: 32.56202127210676
          - type: ap_weighted
            value: 32.56202127210676
          - type: f1
            value: 64.10866172582675
          - type: f1_weighted
            value: 73.08892104263525
          - type: main_score
            value: 70.19402985074626
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB AmazonPolarityClassification (default)
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
          split: test
          type: mteb/amazon_polarity
        metrics:
          - type: accuracy
            value: 68.72295000000001
          - type: ap
            value: 63.50967718714144
          - type: ap_weighted
            value: 63.50967718714144
          - type: f1
            value: 68.34020808585744
          - type: f1_weighted
            value: 68.34020808585744
          - type: main_score
            value: 68.72295000000001
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonReviewsClassification (en)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 31.691999999999997
          - type: f1
            value: 31.085241040043943
          - type: f1_weighted
            value: 31.085241040043943
          - type: main_score
            value: 31.691999999999997
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB ArguAna (default)
          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
          split: test
          type: mteb/arguana
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          - type: map_at_10
            value: 32.775999999999996
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            value: 34.026
          - type: map_at_1000
            value: 34.059
          - type: map_at_20
            value: 33.588
          - type: map_at_3
            value: 28.615000000000002
          - type: map_at_5
            value: 30.824
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            value: 9.886000000000001
          - type: recall_at_1
            value: 20.341
          - type: recall_at_10
            value: 63.798
          - type: recall_at_100
            value: 91.607
          - type: recall_at_1000
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          - type: recall_at_20
            value: 75.249
          - type: recall_at_3
            value: 39.757999999999996
          - type: recall_at_5
            value: 49.431000000000004
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ArxivClusteringP2P (default)
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
          split: test
          type: mteb/arxiv-clustering-p2p
        metrics:
          - type: main_score
            value: 32.95868401684924
          - type: v_measure
            value: 32.95868401684924
          - type: v_measure_std
            value: 14.056211834923879
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB ArxivClusteringS2S (default)
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
          split: test
          type: mteb/arxiv-clustering-s2s
        metrics:
          - type: main_score
            value: 21.233292246705687
          - type: v_measure
            value: 21.233292246705687
          - type: v_measure_std
            value: 15.425917066814412
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB AskUbuntuDupQuestions (default)
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
          split: test
          type: mteb/askubuntudupquestions-reranking
        metrics:
          - type: main_score
            value: 53.563651922608045
          - type: map
            value: 53.563651922608045
          - type: mrr
            value: 68.38906476718111
          - type: nAUC_map_diff1
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          - type: nAUC_map_max
            value: 14.344767912076911
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          - type: nAUC_mrr_diff1
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          - type: nAUC_mrr_max
            value: 18.522973771138822
          - type: nAUC_mrr_std
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        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB BIOSSES (default)
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
          split: test
          type: mteb/biosses-sts
        metrics:
          - type: cosine_pearson
            value: 78.15156521309085
          - type: cosine_spearman
            value: 75.03966163682449
          - type: euclidean_pearson
            value: 51.743736432098494
          - type: euclidean_spearman
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          - type: main_score
            value: 75.03966163682449
          - type: manhattan_pearson
            value: 49.81487823004435
          - type: manhattan_spearman
            value: 50.90236466418698
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB Banking77Classification (default)
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
          split: test
          type: mteb/banking77
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          - type: f1_weighted
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          - type: main_score
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        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB BiorxivClusteringP2P (default)
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
          split: test
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        task:
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      - dataset:
          config: default
          name: MTEB BiorxivClusteringS2S (default)
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
          split: test
          type: mteb/biorxiv-clustering-s2s
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        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB CQADupstackAndroidRetrieval (default)
          revision: f46a197baaae43b4f621051089b82a364682dfeb
          split: test
          type: mteb/cqadupstack-android
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        task:
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      - dataset:
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          name: MTEB MTOPDomainClassification (en)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
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        task:
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      - dataset:
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          name: MTEB MTOPIntentClassification (en)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
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        task:
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      - dataset:
          config: en
          name: MTEB MassiveIntentClassification (en)
          revision: 4672e20407010da34463acc759c162ca9734bca6
          split: test
          type: mteb/amazon_massive_intent
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        task:
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      - dataset:
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          revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
          split: test
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        task:
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      - dataset:
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          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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          type: mteb/medrxiv-clustering-p2p
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        task:
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      - dataset:
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          name: MTEB MedrxivClusteringS2S (default)
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
          split: test
          type: mteb/medrxiv-clustering-s2s
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        task:
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      - dataset:
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          revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
          split: test
          type: mteb/mind_small
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        task:
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          name: MTEB NFCorpus (default)
          revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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          type: mteb/nfcorpus
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          split: test
          type: mteb/sickr-sts
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          - type: cosine_pearson
            value: 74.47186624461347
          - type: cosine_spearman
            value: 63.91365796295238
          - type: euclidean_pearson
            value: 64.47379282614025
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          - type: main_score
            value: 63.91365796295238
          - type: manhattan_pearson
            value: 63.78641984214034
          - type: manhattan_spearman
            value: 58.1628493915856
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SciDocsRR (default)
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
          split: test
          type: mteb/scidocs-reranking
        metrics:
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            value: 70.68076882532134
          - type: map
            value: 70.68076882532134
          - type: mrr
            value: 89.99516188241678
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          - type: nAUC_mrr_diff1
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          - type: nAUC_mrr_max
            value: 76.08848787865979
          - type: nAUC_mrr_std
            value: 72.73263647015558
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SciFact (default)
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
          split: test
          type: mteb/scifact
        metrics:
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          - type: map_at_10
            value: 43.265
          - type: map_at_100
            value: 44.289
          - type: map_at_1000
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            value: 43.855
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          - type: nauc_ndcg_at_5_std
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          - type: nauc_precision_at_1000_std
            value: 45.03378524845005
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          - type: nauc_precision_at_10_max
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            value: 41.887774560054275
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            value: 25.046884850806233
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            value: 44.05189249844107
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            value: 56.07575711551889
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            value: 15.984701343858864
          - type: nauc_recall_at_10_diff1
            value: 42.17980666307712
          - type: nauc_recall_at_10_max
            value: 42.410562357398085
          - type: nauc_recall_at_10_std
            value: 0.5085324911166664
          - type: nauc_recall_at_1_diff1
            value: 57.56838727422756
          - type: nauc_recall_at_1_max
            value: 33.91693407903183
          - type: nauc_recall_at_1_std
            value: -12.559053818648572
          - type: nauc_recall_at_20_diff1
            value: 41.4295207526725
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            value: 47.09186640937312
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            value: 8.914121841807814
          - type: nauc_recall_at_3_diff1
            value: 49.71735118306758
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            value: 40.79913675597018
          - type: nauc_recall_at_3_std
            value: -2.6560764286787197
          - type: nauc_recall_at_5_diff1
            value: 44.40157202097473
          - type: nauc_recall_at_5_max
            value: 41.81658165643147
          - type: nauc_recall_at_5_std
            value: -0.9819402152656556
          - type: ndcg_at_1
            value: 36.667
          - type: ndcg_at_10
            value: 48.231
          - type: ndcg_at_100
            value: 53.077
          - type: ndcg_at_1000
            value: 54.529
          - type: ndcg_at_20
            value: 50.202000000000005
          - type: ndcg_at_3
            value: 42.296
          - type: ndcg_at_5
            value: 45.540000000000006
          - type: precision_at_1
            value: 36.667
          - type: precision_at_10
            value: 6.933
          - type: precision_at_100
            value: 0.95
          - type: precision_at_1000
            value: 0.109
          - type: precision_at_20
            value: 3.9170000000000003
          - type: precision_at_3
            value: 16.889000000000003
          - type: precision_at_5
            value: 11.933
          - type: recall_at_1
            value: 34.721999999999994
          - type: recall_at_10
            value: 62.428
          - type: recall_at_100
            value: 84.806
          - type: recall_at_1000
            value: 96.1
          - type: recall_at_20
            value: 69.828
          - type: recall_at_3
            value: 46.417
          - type: recall_at_5
            value: 54.289
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SprintDuplicateQuestions (default)
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
          split: test
          type: mteb/sprintduplicatequestions-pairclassification
        metrics:
          - type: cosine_accuracy
            value: 99.69900990099009
          - type: cosine_accuracy_threshold
            value: 76.38404965400696
          - type: cosine_ap
            value: 89.62871668005243
          - type: cosine_f1
            value: 84.05797101449276
          - type: cosine_f1_threshold
            value: 75.10095834732056
          - type: cosine_precision
            value: 87.1244635193133
          - type: cosine_recall
            value: 81.2
          - type: dot_accuracy
            value: 99.06534653465346
          - type: dot_accuracy_threshold
            value: 67162.48779296875
          - type: dot_ap
            value: 32.76318972787881
          - type: dot_f1
            value: 36.930860033726816
          - type: dot_f1_threshold
            value: 39663.446044921875
          - type: dot_precision
            value: 31.924198250728864
          - type: dot_recall
            value: 43.8
          - type: euclidean_accuracy
            value: 99.44257425742575
          - type: euclidean_accuracy_threshold
            value: 1320.8951950073242
          - type: euclidean_ap
            value: 70.6030643455353
          - type: euclidean_f1
            value: 68.96551724137932
          - type: euclidean_f1_threshold
            value: 1433.2849502563477
          - type: euclidean_precision
            value: 73.44632768361582
          - type: euclidean_recall
            value: 65
          - type: main_score
            value: 89.62871668005243
          - type: manhattan_accuracy
            value: 99.43564356435644
          - type: manhattan_accuracy_threshold
            value: 23616.18194580078
          - type: manhattan_ap
            value: 69.53528871039842
          - type: manhattan_f1
            value: 67.80032912781131
          - type: manhattan_f1_threshold
            value: 24650.78887939453
          - type: manhattan_precision
            value: 75.09113001215067
          - type: manhattan_recall
            value: 61.8
          - type: max_accuracy
            value: 99.69900990099009
          - type: max_ap
            value: 89.62871668005243
          - type: max_f1
            value: 84.05797101449276
          - type: max_precision
            value: 87.1244635193133
          - type: max_recall
            value: 81.2
          - type: similarity_accuracy
            value: 99.69900990099009
          - type: similarity_accuracy_threshold
            value: 76.38405561447144
          - type: similarity_ap
            value: 89.62871668005243
          - type: similarity_f1
            value: 84.05797101449276
          - type: similarity_f1_threshold
            value: 75.10097026824951
          - type: similarity_precision
            value: 87.1244635193133
          - type: similarity_recall
            value: 81.2
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB StackExchangeClustering (default)
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
          split: test
          type: mteb/stackexchange-clustering
        metrics:
          - type: main_score
            value: 39.45192779241171
          - type: v_measure
            value: 39.45192779241171
          - type: v_measure_std
            value: 4.925771037453908
        task:
          type: Clustering

🪲 brown-beetle-base-v1.1 Model Card

Beetles are some of the most diverse and interesting creatures on Earth. They are found in every environment, from the deepest oceans to the highest mountains. They are also known for their ability to adapt to a wide range of habitats and lifestyles. They are small, fast and powerful!

The beetle series of models are made as good starting points for Static Embedding training (via TokenLearn or Fine-tuning), as well as decent Static Embedding models. Each beetle model is made to be an improvement over the original M2V_base_output model in some way, and that's the threshold we set for each model (except the brown beetle series, which is the original model).

This model has been distilled from baai/bge-base-en-v1.5, with PCA with 768 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model.

The brown beetle series is made for convinience in loading and using the model instead of having to run it, though it is pretty fast to reproduce anyways. If you want to use the original model by the folks from the Minish Lab, you can use the M2V_base_output model.

Version Information

• brown-beetle-base-v0: The original model, without using PCA or Zipf. The lack of PCA and Zipf also makes this a decent model for further training.
• brown-beetle-base-v0.1: The original model, with PCA but of the same size as the original model. This model is great if you want to experiment with Zipf or other weighting methods.
• brown-beetle-base-v1: The original model, with PCA and Zipf.
• brown-beetle-small-v1: A smaller version of the original model, with PCA and Zipf. Equivalent to M2V_base_output.
• brown-beetle-tiny-v1: A tiny version of the original model, with PCA and Zipf.
• brown-beetle-base-v1.1: The original model, with PCA with 768 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model.
• brown-beetle-small-v1.1: A smaller version of the original model, with PCA with 256 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model but slightly worse than the brown-beetle-base-v1.1 model.
• brown-beetle-tiny-v1.1: A tiny version of the original model, with PCA with 128 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model but slightly worse than the brown-beetle-small-v1.1 model.

Installation

Install model2vec using pip:

pip install model2vec

Usage

Load this model using the from_pretrained method:

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("bhavnicksm/brown-beetle-base-v1.1")

# Compute text embeddings
embeddings = model.encode(["Example sentence"])

Read more about the Model2Vec library here.

Comparison with other models

Coming soon...

Acknowledgements

This model is made using the Model2Vec library. Credit goes to the Minish Lab team for developing this library.

Citation

Please cite the Model2Vec repository if you use this model in your work.

@software{minishlab2024model2vec,
  authors = {Stephan Tulkens, Thomas van Dongen},
  title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model},
  year = {2024},
  url = {https://github.com/MinishLab/model2vec},
}