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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: bclavie/mini-base |
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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 Small |
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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: 0.28 |
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name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
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value: 0.38 |
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name: Maxsim Accuracy@3 |
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- type: MaxSim_accuracy@5 |
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value: 0.52 |
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|
name: Maxsim Accuracy@5 |
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- type: MaxSim_accuracy@10 |
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value: 0.64 |
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name: Maxsim Accuracy@10 |
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- type: MaxSim_precision@1 |
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value: 0.28 |
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|
name: Maxsim Precision@1 |
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- type: MaxSim_precision@3 |
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value: 0.14666666666666667 |
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name: Maxsim Precision@3 |
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- type: MaxSim_precision@5 |
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value: 0.12 |
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name: Maxsim Precision@5 |
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- type: MaxSim_precision@10 |
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value: 0.08 |
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name: Maxsim Precision@10 |
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- type: MaxSim_recall@1 |
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value: 0.13166666666666665 |
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|
name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
|
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value: 0.21 |
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|
name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
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value: 0.265 |
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name: Maxsim Recall@5 |
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- type: MaxSim_recall@10 |
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value: 0.335 |
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name: Maxsim Recall@10 |
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- type: MaxSim_ndcg@10 |
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value: 0.27666051264859415 |
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name: Maxsim Ndcg@10 |
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- type: MaxSim_mrr@10 |
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value: 0.3671349206349206 |
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name: Maxsim Mrr@10 |
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- type: MaxSim_map@100 |
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value: 0.22158617300046946 |
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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: 0.8 |
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name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
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value: 0.88 |
|
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name: Maxsim Accuracy@3 |
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- type: MaxSim_accuracy@5 |
|
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value: 0.92 |
|
|
name: Maxsim Accuracy@5 |
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- type: MaxSim_accuracy@10 |
|
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value: 0.96 |
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name: Maxsim Accuracy@10 |
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- type: MaxSim_precision@1 |
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value: 0.8 |
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name: Maxsim Precision@1 |
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- type: MaxSim_precision@3 |
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value: 0.6333333333333332 |
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name: Maxsim Precision@3 |
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- type: MaxSim_precision@5 |
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value: 0.556 |
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name: Maxsim Precision@5 |
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- type: MaxSim_precision@10 |
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value: 0.48399999999999993 |
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name: Maxsim Precision@10 |
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- type: MaxSim_recall@1 |
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value: 0.10583280294731091 |
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name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
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value: 0.1747980000610803 |
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name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
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value: 0.2211728749541224 |
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name: Maxsim Recall@5 |
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- type: MaxSim_recall@10 |
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value: 0.3392671917074792 |
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name: Maxsim Recall@10 |
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- type: MaxSim_ndcg@10 |
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value: 0.6189072509940752 |
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name: Maxsim Ndcg@10 |
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- type: MaxSim_mrr@10 |
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value: 0.8510238095238097 |
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name: Maxsim Mrr@10 |
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- type: MaxSim_map@100 |
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value: 0.47586135688175013 |
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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: 0.88 |
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name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
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value: 0.96 |
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name: Maxsim Accuracy@3 |
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- type: MaxSim_accuracy@5 |
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value: 1.0 |
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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: 0.88 |
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name: Maxsim Precision@1 |
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- type: MaxSim_precision@3 |
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value: 0.33333333333333326 |
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name: Maxsim Precision@3 |
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- type: MaxSim_precision@5 |
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value: 0.21199999999999997 |
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name: Maxsim Precision@5 |
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- type: MaxSim_precision@10 |
|
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value: 0.10799999999999997 |
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name: Maxsim Precision@10 |
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- type: MaxSim_recall@1 |
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value: 0.8166666666666668 |
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name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
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value: 0.9133333333333333 |
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name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
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value: 0.9633333333333333 |
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name: Maxsim Recall@5 |
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- type: MaxSim_recall@10 |
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value: 0.9733333333333333 |
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name: Maxsim Recall@10 |
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- type: MaxSim_ndcg@10 |
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value: 0.9208334669406996 |
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name: Maxsim Ndcg@10 |
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- type: MaxSim_mrr@10 |
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value: 0.929 |
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name: Maxsim Mrr@10 |
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- type: MaxSim_map@100 |
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value: 0.8912380952380953 |
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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: 0.44 |
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name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
|
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value: 0.56 |
|
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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: 0.76 |
|
|
name: Maxsim Accuracy@10 |
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- type: MaxSim_precision@1 |
|
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value: 0.44 |
|
|
name: Maxsim Precision@1 |
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- type: MaxSim_precision@3 |
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value: 0.26666666666666666 |
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name: Maxsim Precision@3 |
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- type: MaxSim_precision@5 |
|
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value: 0.22 |
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name: Maxsim Precision@5 |
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- type: MaxSim_precision@10 |
|
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value: 0.13599999999999998 |
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name: Maxsim Precision@10 |
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- type: MaxSim_recall@1 |
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value: 0.22257936507936507 |
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name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
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value: 0.36418253968253966 |
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name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
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value: 0.5042063492063492 |
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name: Maxsim Recall@5 |
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- type: MaxSim_recall@10 |
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value: 0.5963968253968254 |
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name: Maxsim Recall@10 |
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- type: MaxSim_ndcg@10 |
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value: 0.4781894440800092 |
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name: Maxsim Ndcg@10 |
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- type: MaxSim_mrr@10 |
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value: 0.5321666666666666 |
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name: Maxsim Mrr@10 |
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- type: MaxSim_map@100 |
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value: 0.39543817074336585 |
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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: 0.84 |
|
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name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
|
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value: 0.96 |
|
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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: 0.98 |
|
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name: Maxsim Accuracy@10 |
|
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- type: MaxSim_precision@1 |
|
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value: 0.84 |
|
|
name: Maxsim Precision@1 |
|
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- type: MaxSim_precision@3 |
|
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value: 0.5066666666666666 |
|
|
name: Maxsim Precision@3 |
|
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- type: MaxSim_precision@5 |
|
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value: 0.324 |
|
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name: Maxsim Precision@5 |
|
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- type: MaxSim_precision@10 |
|
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value: 0.17399999999999996 |
|
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name: Maxsim Precision@10 |
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- type: MaxSim_recall@1 |
|
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value: 0.42 |
|
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name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
|
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value: 0.76 |
|
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name: Maxsim Recall@3 |
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- type: MaxSim_recall@5 |
|
|
value: 0.81 |
|
|
name: Maxsim Recall@5 |
|
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- type: MaxSim_recall@10 |
|
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value: 0.87 |
|
|
name: Maxsim Recall@10 |
|
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- type: MaxSim_ndcg@10 |
|
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value: 0.813477163259318 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
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value: 0.8973333333333333 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
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value: 0.7469519155158202 |
|
|
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: 0.48 |
|
|
name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
|
|
value: 0.64 |
|
|
name: Maxsim Accuracy@3 |
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- type: MaxSim_accuracy@5 |
|
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value: 0.68 |
|
|
name: Maxsim Accuracy@5 |
|
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- type: MaxSim_accuracy@10 |
|
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value: 0.8 |
|
|
name: Maxsim Accuracy@10 |
|
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- type: MaxSim_precision@1 |
|
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value: 0.48 |
|
|
name: Maxsim Precision@1 |
|
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- type: MaxSim_precision@3 |
|
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value: 0.21333333333333332 |
|
|
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 |
|
|
value: 0.08 |
|
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name: Maxsim Precision@10 |
|
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- type: MaxSim_recall@1 |
|
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value: 0.48 |
|
|
name: Maxsim Recall@1 |
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- type: MaxSim_recall@3 |
|
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value: 0.64 |
|
|
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 |
|
|
value: 0.8 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.6277729303272284 |
|
|
name: Maxsim Ndcg@10 |
|
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- type: MaxSim_mrr@10 |
|
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value: 0.574547619047619 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
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value: 0.585980942367483 |
|
|
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: 0.5 |
|
|
name: Maxsim Accuracy@1 |
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- type: MaxSim_accuracy@3 |
|
|
value: 0.62 |
|
|
name: Maxsim Accuracy@3 |
|
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- type: MaxSim_accuracy@5 |
|
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value: 0.64 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.74 |
|
|
name: Maxsim Accuracy@10 |
|
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- type: MaxSim_precision@1 |
|
|
value: 0.5 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.4 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.3440000000000001 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.292 |
|
|
name: Maxsim Precision@10 |
|
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- type: MaxSim_recall@1 |
|
|
value: 0.06602691624937523 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.09818050757008642 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.11806464030634821 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.15514192209178235 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.37561452677051027 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.5691666666666667 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.18300178358234423 |
|
|
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: 0.56 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.76 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.78 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.82 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.56 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.2533333333333333 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.16399999999999998 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.088 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.54 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.71 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.75 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.79 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.6818400710905007 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.6597222222222223 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.6459882013890279 |
|
|
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: 0.86 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.98 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.98 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 1.0 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.86 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.40666666666666657 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.264 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.13799999999999998 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.764 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.9453333333333334 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.97 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.9966666666666666 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.9414269581610836 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.9228571428571428 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.9205543345543344 |
|
|
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: 0.5 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.74 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.76 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.82 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.5 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.3933333333333333 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.292 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.18599999999999997 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.10466666666666667 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.24366666666666664 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.29966666666666664 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.3826666666666666 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.39084995006976664 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.6197222222222222 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.3153590016638529 |
|
|
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: 0.28 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.56 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.64 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.84 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.28 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.18666666666666668 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.12800000000000003 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.08399999999999999 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.28 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.56 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.64 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.84 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.5432952971404568 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.450484126984127 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.4551681906230779 |
|
|
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: 0.7 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.82 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.84 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.88 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.7 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.29333333333333333 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.18799999999999997 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.09799999999999998 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.665 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.81 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.84 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.87 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.7883940477308562 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.7645238095238096 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.7622104923007755 |
|
|
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: 0.673469387755102 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.9183673469387755 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.9591836734693877 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 1.0 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.673469387755102 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.6326530612244898 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.6040816326530614 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.5020408163265305 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.04919462393895531 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.13143050077268048 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.20505385244507174 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.3259510245836729 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.5631037374817277 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.7906462585034014 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.41297955687388305 |
|
|
name: Maxsim Map@100 |
|
|
- task: |
|
|
type: nano-beir |
|
|
name: Nano BEIR |
|
|
dataset: |
|
|
name: NanoBEIR mean |
|
|
type: NanoBEIR_mean |
|
|
metrics: |
|
|
- type: MaxSim_accuracy@1 |
|
|
value: 0.5994976452119309 |
|
|
name: Maxsim Accuracy@1 |
|
|
- type: MaxSim_accuracy@3 |
|
|
value: 0.7521821036106752 |
|
|
name: Maxsim Accuracy@3 |
|
|
- type: MaxSim_accuracy@5 |
|
|
value: 0.7983987441130298 |
|
|
name: Maxsim Accuracy@5 |
|
|
- type: MaxSim_accuracy@10 |
|
|
value: 0.8646153846153847 |
|
|
name: Maxsim Accuracy@10 |
|
|
- type: MaxSim_precision@1 |
|
|
value: 0.5994976452119309 |
|
|
name: Maxsim Precision@1 |
|
|
- type: MaxSim_precision@3 |
|
|
value: 0.3589220303506017 |
|
|
name: Maxsim Precision@3 |
|
|
- type: MaxSim_precision@5 |
|
|
value: 0.2732370486656201 |
|
|
name: Maxsim Precision@5 |
|
|
- type: MaxSim_precision@10 |
|
|
value: 0.18846467817896384 |
|
|
name: Maxsim Precision@10 |
|
|
- type: MaxSim_recall@1 |
|
|
value: 0.35735643909346215 |
|
|
name: Maxsim Recall@1 |
|
|
- type: MaxSim_recall@3 |
|
|
value: 0.5046865293399786 |
|
|
name: Maxsim Recall@3 |
|
|
- type: MaxSim_recall@5 |
|
|
value: 0.5589613628393763 |
|
|
name: Maxsim Recall@5 |
|
|
- type: MaxSim_recall@10 |
|
|
value: 0.6364941254189559 |
|
|
name: Maxsim Recall@10 |
|
|
- type: MaxSim_ndcg@10 |
|
|
value: 0.6169511812842173 |
|
|
name: Maxsim Ndcg@10 |
|
|
- type: MaxSim_mrr@10 |
|
|
value: 0.6867945229373801 |
|
|
name: Maxsim Mrr@10 |
|
|
- type: MaxSim_map@100 |
|
|
value: 0.5394090934410984 |
|
|
name: Maxsim Map@100 |
|
|
--- |
|
|
|
|
|
# ColBERT MUVERA Small |
|
|
|
|
|
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [bclavie/mini-base](https://huggingface.co/bclavie/mini-base) 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( |
|
|
path="neuml/colbert-muvera-small", |
|
|
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-small", 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-small", |
|
|
) |
|
|
|
|
|
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': 384, '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 | 0.5995 | |
|
|
| MaxSim_accuracy@3 | 0.7522 | |
|
|
| MaxSim_accuracy@5 | 0.7984 | |
|
|
| MaxSim_accuracy@10 | 0.8646 | |
|
|
| MaxSim_precision@1 | 0.5995 | |
|
|
| MaxSim_precision@3 | 0.3589 | |
|
|
| MaxSim_precision@5 | 0.2732 | |
|
|
| MaxSim_precision@10 | 0.1885 | |
|
|
| MaxSim_recall@1 | 0.3574 | |
|
|
| MaxSim_recall@3 | 0.5047 | |
|
|
| MaxSim_recall@5 | 0.559 | |
|
|
| MaxSim_recall@10 | 0.6365 | |
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| **MaxSim_ndcg@10** | **0.617** | |
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| MaxSim_mrr@10 | 0.6868 | |
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| MaxSim_map@100 | 0.5394 | |
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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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- `gradient_accumulation_steps`: 4 |
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- `learning_rate`: 3e-06 |
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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`: 8 |
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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`: 4 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 3e-06 |
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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 |
|
|
- `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 |
|
|
- `hub_always_push`: False |
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|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
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|
- `include_inputs_for_metrics`: False |
|
|
- `include_for_metrics`: [] |
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|
- `eval_do_concat_batches`: True |
|
|
- `fp16_backend`: auto |
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|
- `push_to_hub_model_id`: None |
|
|
- `push_to_hub_organization`: None |
|
|
- `mp_parameters`: |
|
|
- `auto_find_batch_size`: False |
|
|
- `full_determinism`: False |
|
|
- `torchdynamo`: None |
|
|
- `ray_scope`: last |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
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|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
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|
- `optim_target_modules`: None |
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|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: batch_sampler |
|
|
- `multi_dataset_batch_sampler`: proportional |
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|
|
|
|
</details> |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.10.18 |
|
|
- Sentence Transformers: 4.0.2 |
|
|
- PyLate: 1.3.0 |
|
|
- Transformers: 4.52.3 |
|
|
- PyTorch: 2.8.0+cu128 |
|
|
- Accelerate: 1.10.1 |
|
|
- Datasets: 4.0.0 |
|
|
- 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" |
|
|
} |
|
|
``` |
|
|
|
|
|
#### 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} |
|
|
} |
|
|
``` |
|
|
|
