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--- |
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tags: |
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- ColBERT |
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- PyLate |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:640000 |
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- loss:Distillation |
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base_model: google/bert_uncased_L-2_H-128_A-2 |
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datasets: |
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- lightonai/ms-marco-en-bge-gemma-unnormalized |
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pipeline_tag: sentence-similarity |
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library_name: PyLate |
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license: apache-2.0 |
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metrics: |
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- MaxSim_accuracy@1 |
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- MaxSim_accuracy@3 |
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- MaxSim_accuracy@5 |
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- MaxSim_accuracy@10 |
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- MaxSim_precision@1 |
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- MaxSim_precision@3 |
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- MaxSim_precision@5 |
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- MaxSim_precision@10 |
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- MaxSim_recall@1 |
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- MaxSim_recall@3 |
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- MaxSim_recall@5 |
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- MaxSim_recall@10 |
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- MaxSim_ndcg@10 |
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- MaxSim_mrr@10 |
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- MaxSim_map@100 |
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model-index: |
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- name: ColBERT MUVERA Micro |
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results: |
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- task: |
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type: py-late-information-retrieval |
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name: Py Late Information Retrieval |
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dataset: |
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name: NanoClimateFEVER |
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type: NanoClimateFEVER |
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metrics: |
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- type: MaxSim_accuracy@1 |
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value: 0.26 |
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name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
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value: 0.36 |
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name: Maxsim Accuracy@3 |
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- type: MaxSim_accuracy@5 |
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value: 0.4 |
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|
name: Maxsim Accuracy@5 |
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- type: MaxSim_accuracy@10 |
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value: 0.58 |
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name: Maxsim Accuracy@10 |
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- type: MaxSim_precision@1 |
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value: 0.26 |
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name: Maxsim Precision@1 |
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- type: MaxSim_precision@3 |
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value: 0.12666666666666665 |
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name: Maxsim Precision@3 |
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- type: MaxSim_precision@5 |
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value: 0.092 |
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name: Maxsim Precision@5 |
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- type: MaxSim_precision@10 |
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value: 0.07800000000000001 |
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name: Maxsim Precision@10 |
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- type: MaxSim_recall@1 |
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value: 0.11233333333333333 |
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|
name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
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value: 0.16066666666666665 |
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name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
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value: 0.184 |
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name: Maxsim Recall@5 |
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- type: MaxSim_recall@10 |
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value: 0.3206666666666667 |
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name: Maxsim Recall@10 |
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- type: MaxSim_ndcg@10 |
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value: 0.24408616743142095 |
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name: Maxsim Ndcg@10 |
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- type: MaxSim_mrr@10 |
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value: 0.33196825396825397 |
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name: Maxsim Mrr@10 |
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- type: MaxSim_map@100 |
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value: 0.18128382432733356 |
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name: Maxsim Map@100 |
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- task: |
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type: py-late-information-retrieval |
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name: Py Late Information Retrieval |
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dataset: |
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name: NanoDBPedia |
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type: NanoDBPedia |
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metrics: |
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- type: MaxSim_accuracy@1 |
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value: 0.68 |
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name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
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value: 0.86 |
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name: Maxsim Accuracy@3 |
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- type: MaxSim_accuracy@5 |
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value: 0.92 |
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name: Maxsim Accuracy@5 |
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- type: MaxSim_accuracy@10 |
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value: 0.94 |
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name: Maxsim Accuracy@10 |
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- type: MaxSim_precision@1 |
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value: 0.68 |
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name: Maxsim Precision@1 |
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- type: MaxSim_precision@3 |
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value: 0.6066666666666667 |
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name: Maxsim Precision@3 |
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- type: MaxSim_precision@5 |
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value: 0.56 |
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name: Maxsim Precision@5 |
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- type: MaxSim_precision@10 |
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value: 0.502 |
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name: Maxsim Precision@10 |
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- type: MaxSim_recall@1 |
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value: 0.05322585293904511 |
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name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
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value: 0.16789568954347403 |
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name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
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value: 0.22988072374930787 |
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name: Maxsim Recall@5 |
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- type: MaxSim_recall@10 |
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value: 0.35043982767195947 |
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name: Maxsim Recall@10 |
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- type: MaxSim_ndcg@10 |
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value: 0.6003406576207015 |
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name: Maxsim Ndcg@10 |
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- type: MaxSim_mrr@10 |
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value: 0.7850000000000001 |
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name: Maxsim Mrr@10 |
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- type: MaxSim_map@100 |
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value: 0.4687280514608297 |
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name: Maxsim Map@100 |
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- task: |
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type: py-late-information-retrieval |
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name: Py Late Information Retrieval |
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dataset: |
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name: NanoFEVER |
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type: NanoFEVER |
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metrics: |
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- type: MaxSim_accuracy@1 |
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value: 0.72 |
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name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
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value: 0.78 |
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name: Maxsim Accuracy@3 |
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- type: MaxSim_accuracy@5 |
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value: 0.84 |
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name: Maxsim Accuracy@5 |
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- type: MaxSim_accuracy@10 |
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value: 0.9 |
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name: Maxsim Accuracy@10 |
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- type: MaxSim_precision@1 |
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value: 0.72 |
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name: Maxsim Precision@1 |
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- type: MaxSim_precision@3 |
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value: 0.2733333333333333 |
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name: Maxsim Precision@3 |
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- type: MaxSim_precision@5 |
|
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value: 0.18 |
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name: Maxsim Precision@5 |
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- type: MaxSim_precision@10 |
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value: 0.1 |
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name: Maxsim Precision@10 |
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- type: MaxSim_recall@1 |
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value: 0.6866666666666668 |
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name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
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value: 0.7633333333333333 |
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name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
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value: 0.82 |
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name: Maxsim Recall@5 |
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- type: MaxSim_recall@10 |
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value: 0.89 |
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name: Maxsim Recall@10 |
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- type: MaxSim_ndcg@10 |
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value: 0.7955242043086649 |
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name: Maxsim Ndcg@10 |
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- type: MaxSim_mrr@10 |
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value: 0.7731666666666667 |
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name: Maxsim Mrr@10 |
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- type: MaxSim_map@100 |
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value: 0.7676133768765347 |
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name: Maxsim Map@100 |
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- task: |
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type: py-late-information-retrieval |
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name: Py Late Information Retrieval |
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dataset: |
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name: NanoFiQA2018 |
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type: NanoFiQA2018 |
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metrics: |
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- type: MaxSim_accuracy@1 |
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value: 0.3 |
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name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
|
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value: 0.54 |
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name: Maxsim Accuracy@3 |
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- type: MaxSim_accuracy@5 |
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value: 0.58 |
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name: Maxsim Accuracy@5 |
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- type: MaxSim_accuracy@10 |
|
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value: 0.66 |
|
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name: Maxsim Accuracy@10 |
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- type: MaxSim_precision@1 |
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value: 0.3 |
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|
name: Maxsim Precision@1 |
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- type: MaxSim_precision@3 |
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value: 0.2333333333333333 |
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name: Maxsim Precision@3 |
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- type: MaxSim_precision@5 |
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value: 0.17200000000000004 |
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name: Maxsim Precision@5 |
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- type: MaxSim_precision@10 |
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value: 0.10800000000000001 |
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name: Maxsim Precision@10 |
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- type: MaxSim_recall@1 |
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value: 0.1770793650793651 |
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name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
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value: 0.3453492063492064 |
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name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
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value: 0.4009047619047619 |
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name: Maxsim Recall@5 |
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- type: MaxSim_recall@10 |
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value: 0.4740952380952381 |
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name: Maxsim Recall@10 |
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- type: MaxSim_ndcg@10 |
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value: 0.38709436118795515 |
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name: Maxsim Ndcg@10 |
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- type: MaxSim_mrr@10 |
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value: 0.4288015873015872 |
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name: Maxsim Mrr@10 |
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- type: MaxSim_map@100 |
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value: 0.3297000135708943 |
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name: Maxsim Map@100 |
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- task: |
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type: py-late-information-retrieval |
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name: Py Late Information Retrieval |
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dataset: |
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name: NanoHotpotQA |
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type: NanoHotpotQA |
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metrics: |
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- type: MaxSim_accuracy@1 |
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value: 0.94 |
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name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
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value: 0.94 |
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name: Maxsim Accuracy@3 |
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- type: MaxSim_accuracy@5 |
|
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value: 0.98 |
|
|
name: Maxsim Accuracy@5 |
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- type: MaxSim_accuracy@10 |
|
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value: 1.0 |
|
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name: Maxsim Accuracy@10 |
|
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- type: MaxSim_precision@1 |
|
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value: 0.94 |
|
|
name: Maxsim Precision@1 |
|
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- type: MaxSim_precision@3 |
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value: 0.5 |
|
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name: Maxsim Precision@3 |
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- type: MaxSim_precision@5 |
|
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value: 0.31200000000000006 |
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name: Maxsim Precision@5 |
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- type: MaxSim_precision@10 |
|
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value: 0.16599999999999995 |
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name: Maxsim Precision@10 |
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- type: MaxSim_recall@1 |
|
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value: 0.47 |
|
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name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
|
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value: 0.75 |
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name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
|
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value: 0.78 |
|
|
name: Maxsim Recall@5 |
|
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- type: MaxSim_recall@10 |
|
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value: 0.83 |
|
|
name: Maxsim Recall@10 |
|
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- type: MaxSim_ndcg@10 |
|
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value: 0.8179728241272247 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
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value: 0.9512222222222222 |
|
|
name: Maxsim Mrr@10 |
|
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- type: MaxSim_map@100 |
|
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value: 0.7611883462001594 |
|
|
name: Maxsim Map@100 |
|
|
- task: |
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type: py-late-information-retrieval |
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name: Py Late Information Retrieval |
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dataset: |
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name: NanoMSMARCO |
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type: NanoMSMARCO |
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metrics: |
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- type: MaxSim_accuracy@1 |
|
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value: 0.42 |
|
|
name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
|
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value: 0.66 |
|
|
name: Maxsim Accuracy@3 |
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- type: MaxSim_accuracy@5 |
|
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value: 0.68 |
|
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name: Maxsim Accuracy@5 |
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- type: MaxSim_accuracy@10 |
|
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value: 0.78 |
|
|
name: Maxsim Accuracy@10 |
|
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- type: MaxSim_precision@1 |
|
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value: 0.42 |
|
|
name: Maxsim Precision@1 |
|
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- type: MaxSim_precision@3 |
|
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value: 0.22 |
|
|
name: Maxsim Precision@3 |
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- type: MaxSim_precision@5 |
|
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value: 0.136 |
|
|
name: Maxsim Precision@5 |
|
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- type: MaxSim_precision@10 |
|
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value: 0.07800000000000001 |
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name: Maxsim Precision@10 |
|
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- type: MaxSim_recall@1 |
|
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value: 0.42 |
|
|
name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
|
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value: 0.66 |
|
|
name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
|
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value: 0.68 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
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value: 0.78 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.5976880189340548 |
|
|
name: Maxsim Ndcg@10 |
|
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- type: MaxSim_mrr@10 |
|
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value: 0.5393809523809523 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
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value: 0.5531015913611822 |
|
|
name: Maxsim Map@100 |
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- task: |
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type: py-late-information-retrieval |
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name: Py Late Information Retrieval |
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dataset: |
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name: NanoNFCorpus |
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type: NanoNFCorpus |
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metrics: |
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- type: MaxSim_accuracy@1 |
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value: 0.46 |
|
|
name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
|
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value: 0.58 |
|
|
name: Maxsim Accuracy@3 |
|
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- type: MaxSim_accuracy@5 |
|
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value: 0.62 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.68 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.46 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.38 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.324 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.272 |
|
|
name: Maxsim Precision@10 |
|
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- type: MaxSim_recall@1 |
|
|
value: 0.04276439372638386 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.07977851865112022 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.11439841040272719 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.1391695106171535 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.34241148621124995 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.5320000000000001 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.14897381866568696 |
|
|
name: Maxsim Map@100 |
|
|
- task: |
|
|
type: py-late-information-retrieval |
|
|
name: Py Late Information Retrieval |
|
|
dataset: |
|
|
name: NanoNQ |
|
|
type: NanoNQ |
|
|
metrics: |
|
|
- type: MaxSim_accuracy@1 |
|
|
value: 0.42 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.68 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.74 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.84 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.42 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.23333333333333328 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.15200000000000002 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.086 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.4 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.66 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.72 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.79 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.6184738987111722 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.5763888888888888 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.5642312927870203 |
|
|
name: Maxsim Map@100 |
|
|
- task: |
|
|
type: py-late-information-retrieval |
|
|
name: Py Late Information Retrieval |
|
|
dataset: |
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name: NanoQuoraRetrieval |
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type: NanoQuoraRetrieval |
|
|
metrics: |
|
|
- type: MaxSim_accuracy@1 |
|
|
value: 0.8 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.92 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.94 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.96 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.8 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.3399999999999999 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.22399999999999998 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.11999999999999998 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.7239999999999999 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.8473333333333334 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.9006666666666666 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.9373333333333334 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.863105292852843 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.8611904761904764 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.8312823701317842 |
|
|
name: Maxsim Map@100 |
|
|
- task: |
|
|
type: py-late-information-retrieval |
|
|
name: Py Late Information Retrieval |
|
|
dataset: |
|
|
name: NanoSCIDOCS |
|
|
type: NanoSCIDOCS |
|
|
metrics: |
|
|
- type: MaxSim_accuracy@1 |
|
|
value: 0.42 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.58 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.64 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.7 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.42 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.2866666666666667 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.20799999999999996 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.138 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.085 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.17666666666666664 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.21366666666666667 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.2826666666666667 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.2889801789850345 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.5005 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.21685607444339383 |
|
|
name: Maxsim Map@100 |
|
|
- task: |
|
|
type: py-late-information-retrieval |
|
|
name: Py Late Information Retrieval |
|
|
dataset: |
|
|
name: NanoArguAna |
|
|
type: NanoArguAna |
|
|
metrics: |
|
|
- type: MaxSim_accuracy@1 |
|
|
value: 0.2 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.44 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.5 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.64 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.2 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.14666666666666664 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.1 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.064 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.2 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.44 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.5 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.64 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.4151392430544827 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.3440555555555555 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.3521906424035335 |
|
|
name: Maxsim Map@100 |
|
|
- task: |
|
|
type: py-late-information-retrieval |
|
|
name: Py Late Information Retrieval |
|
|
dataset: |
|
|
name: NanoSciFact |
|
|
type: NanoSciFact |
|
|
metrics: |
|
|
- type: MaxSim_accuracy@1 |
|
|
value: 0.58 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.76 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.82 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.86 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.58 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.2733333333333333 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.18 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.09399999999999999 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.555 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.735 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.8 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.84 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.7153590631749926 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.6798333333333333 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.6760413640032285 |
|
|
name: Maxsim Map@100 |
|
|
- task: |
|
|
type: py-late-information-retrieval |
|
|
name: Py Late Information Retrieval |
|
|
dataset: |
|
|
name: NanoTouche2020 |
|
|
type: NanoTouche2020 |
|
|
metrics: |
|
|
- type: MaxSim_accuracy@1 |
|
|
value: 0.7551020408163265 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 1.0 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 1.0 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 1.0 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.7551020408163265 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.6734693877551019 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.6000000000000001 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.5285714285714286 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.050375728116040484 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.13379303377518686 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.19744749683082305 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.3328396127707909 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.5927407647152685 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.8639455782312924 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.4115661843314275 |
|
|
name: Maxsim Map@100 |
|
|
- task: |
|
|
type: nano-beir |
|
|
name: Nano BEIR |
|
|
dataset: |
|
|
name: NanoBEIR mean |
|
|
type: NanoBEIR_mean |
|
|
metrics: |
|
|
- type: MaxSim_accuracy@1 |
|
|
value: 0.5350078492935635 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.7000000000000001 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.743076923076923 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.8107692307692307 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.5350078492935635 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.33026687598116167 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.24923076923076928 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.1795824175824176 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.3058804107585258 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.45537049602453755 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.5031511327862271 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.5851700658324468 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.5599166277934665 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.6282656549799407 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.4817505346586929 |
|
|
name: Maxsim Map@100 |
|
|
--- |
|
|
|
|
|
# ColBERT MUVERA Micro |
|
|
|
|
|
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the [msmarco-en-bge-gemma-unnormalized](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma-unnormalized) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. |
|
|
|
|
|
This model is trained with un-normalized scores, making it compatible with [MUVERA fixed-dimensional encoding](https://arxiv.org/abs/2405.19504). |
|
|
|
|
|
## Usage (txtai) |
|
|
|
|
|
This model can be used to build embeddings databases with [txtai](https://github.com/neuml/txtai) for semantic search and/or as a knowledge source for retrieval augmented generation (RAG). |
|
|
|
|
|
_Note: txtai 9.0+ is required for late interaction model support_ |
|
|
|
|
|
```python |
|
|
import txtai |
|
|
|
|
|
embeddings = txtai.Embeddings( |
|
|
sparse="neuml/colbert-muvera-micro", |
|
|
content=True |
|
|
) |
|
|
embeddings.index(documents()) |
|
|
|
|
|
# Run a query |
|
|
embeddings.search("query to run") |
|
|
``` |
|
|
|
|
|
Late interaction models excel as reranker pipelines. |
|
|
|
|
|
```python |
|
|
from txtai.pipeline import Reranker, Similarity |
|
|
|
|
|
similarity = Similarity(path="neuml/colbert-muvera-micro", lateencode=True) |
|
|
ranker = Reranker(embeddings, similarity) |
|
|
ranker("query to run") |
|
|
``` |
|
|
|
|
|
## Usage (PyLate) |
|
|
|
|
|
Alternatively, the model can be loaded with [PyLate](https://github.com/lightonai/pylate). |
|
|
|
|
|
```python |
|
|
from pylate import rank, models |
|
|
|
|
|
queries = [ |
|
|
"query A", |
|
|
"query B", |
|
|
] |
|
|
|
|
|
documents = [ |
|
|
["document A", "document B"], |
|
|
["document 1", "document C", "document B"], |
|
|
] |
|
|
|
|
|
documents_ids = [ |
|
|
[1, 2], |
|
|
[1, 3, 2], |
|
|
] |
|
|
|
|
|
model = models.ColBERT( |
|
|
model_name_or_path="neuml/colbert-muvera-micro", |
|
|
) |
|
|
|
|
|
queries_embeddings = model.encode( |
|
|
queries, |
|
|
is_query=True, |
|
|
) |
|
|
|
|
|
documents_embeddings = model.encode( |
|
|
documents, |
|
|
is_query=False, |
|
|
) |
|
|
|
|
|
reranked_documents = rank.rerank( |
|
|
documents_ids=documents_ids, |
|
|
queries_embeddings=queries_embeddings, |
|
|
documents_embeddings=documents_embeddings, |
|
|
) |
|
|
``` |
|
|
|
|
|
### Full Model Architecture |
|
|
|
|
|
``` |
|
|
ColBERT( |
|
|
(0): Transformer({'max_seq_length': 299, 'do_lower_case': False}) with Transformer model: BertModel |
|
|
(1): Dense({'in_features': 128, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) |
|
|
) |
|
|
``` |
|
|
|
|
|
## Evaluation |
|
|
|
|
|
### BEIR Subset |
|
|
|
|
|
The following table shows a subset of BEIR scored with the [txtai benchmarks script](https://github.com/neuml/txtai/blob/master/examples/benchmarks.py). |
|
|
|
|
|
Scores reported are `ndcg@10` and grouped into the following three categories. |
|
|
|
|
|
#### FULL multi-vector maxsim |
|
|
|
|
|
| Model | Parameters | ArguAna | NFCorpus | SciFact | Average | |
|
|
|:------------------|:-----------|:---------|:---------|:--------|:--------| |
|
|
| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.4440 | 0.3649 | 0.7423 | 0.5171 | |
|
|
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.4595 | 0.3165 | 0.6456 | 0.4739 | |
|
|
| [**ColBERT MUVERA Micro**](https://huggingface.co/neuml/colbert-muvera-micro) | **4M** | **0.3947** | **0.3235** | **0.6676** | **0.4619** | |
|
|
| [ColBERT MUVERA Small](https://huggingface.co/neuml/colbert-muvera-small) | 33M | 0.4455 | 0.3502 | 0.7145 | 0.5034 | |
|
|
| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.4946 | 0.3717 | 0.7529 | 0.5397 | |
|
|
|
|
|
#### MUVERA encoding + maxsim re-ranking of the top 100 results per MUVERA paper |
|
|
|
|
|
| Model | Parameters | ArguAna | NFCorpus | SciFact | Average | |
|
|
|:------------------|:-----------|:---------|:---------|:--------|:--------| |
|
|
| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.0317 | 0.1135 | 0.0836 | 0.0763 | |
|
|
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.4562 | 0.3025 | 0.6278 | 0.4622 | |
|
|
| [**ColBERT MUVERA Micro**](https://huggingface.co/neuml/colbert-muvera-micro) | **4M** | **0.3849** | **0.3095** | **0.6464** | **0.4469** | |
|
|
| [ColBERT MUVERA Small](https://huggingface.co/neuml/colbert-muvera-small) | 33M | 0.4451 | 0.3537 | 0.7148 | 0.5045 | |
|
|
| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.0265 | 0.1052 | 0.0556 | 0.0624 | |
|
|
|
|
|
#### MUVERA encoding only |
|
|
|
|
|
| Model | Parameters | ArguAna | NFCorpus | SciFact | Average | |
|
|
|:------------------|:-----------|:---------|:---------|:--------|:--------| |
|
|
| [AnswerAI ColBERT Small v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 33M | 0.0024 | 0.0201 | 0.0047 | 0.0091 | |
|
|
| [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.3463 | 0.2356 | 0.5002 | 0.3607 | |
|
|
| [**ColBERT MUVERA Micro**](https://huggingface.co/neuml/colbert-muvera-micro) | **4M** | **0.2795** | **0.2348** | **0.4875** | **0.3339** | |
|
|
| [ColBERT MUVERA Small](https://huggingface.co/neuml/colbert-muvera-small) | 33M | 0.3850 | 0.2928 | 0.6357 | 0.4378 | |
|
|
| [GTE ModernColBERT v1](https://huggingface.co/lightonai/GTE-ModernColBERT-v1) | 149M | 0.0003 | 0.0203 |0.0013 | 0.0073 | |
|
|
|
|
|
_Note: The scores reported don't match scores reported in the respective papers due to different default settings in the txtai benchmark scripts._ |
|
|
|
|
|
As noted earlier, models trained with min-max score normalization don't perform well with MUVERA encoding. See this [GitHub Issue](https://github.com/lightonai/pylate/issues/142) for more. |
|
|
|
|
|
**In reviewing the scores, this model is surprisingly and unreasonably competitive with the original ColBERT v2 model at only 3% of the size!** |
|
|
|
|
|
### Nano BEIR |
|
|
* Dataset: `NanoBEIR_mean` |
|
|
* Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code> |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| MaxSim_accuracy@1 | 0.535 | |
|
|
| MaxSim_accuracy@3 | 0.7 | |
|
|
| MaxSim_accuracy@5 | 0.7431 | |
|
|
| MaxSim_accuracy@10 | 0.8108 | |
|
|
| MaxSim_precision@1 | 0.535 | |
|
|
| MaxSim_precision@3 | 0.3303 | |
|
|
| MaxSim_precision@5 | 0.2492 | |
|
|
| MaxSim_precision@10 | 0.1796 | |
|
|
| MaxSim_recall@1 | 0.3059 | |
|
|
| MaxSim_recall@3 | 0.4554 | |
|
|
| MaxSim_recall@5 | 0.5032 | |
|
|
| MaxSim_recall@10 | 0.5852 | |
|
|
| **MaxSim_ndcg@10** | **0.5599** | |
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| MaxSim_mrr@10 | 0.6283 | |
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| MaxSim_map@100 | 0.4818 | |
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## Training Details |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `learning_rate`: 0.0003 |
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- `num_train_epochs`: 1 |
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- `warmup_ratio`: 0.05 |
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- `bf16`: True |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 8 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 0.0003 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.05 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `include_tokens_per_second`: False |
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|
- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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|
- `eval_on_start`: False |
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|
- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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|
- `average_tokens_across_devices`: False |
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|
- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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|
- `multi_dataset_batch_sampler`: proportional |
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</details> |
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|
|
|
|
### Framework Versions |
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|
- Python: 3.10.18 |
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|
- Sentence Transformers: 4.0.2 |
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|
- PyLate: 1.3.0 |
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- Transformers: 4.52.3 |
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|
- PyTorch: 2.8.0+cu128 |
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|
- Accelerate: 1.10.1 |
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- Datasets: 4.0.0 |
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|
- Tokenizers: 0.21.4 |
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|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084" |
|
|
} |
|
|
``` |
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|
|
|
|
#### PyLate |
|
|
```bibtex |
|
|
@misc{PyLate, |
|
|
title={PyLate: Flexible Training and Retrieval for Late Interaction Models}, |
|
|
author={Chaffin, Antoine and Sourty, Raphaël}, |
|
|
url={https://github.com/lightonai/pylate}, |
|
|
year={2024} |
|
|
} |
|
|
``` |
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|
