| | --- |
| | language: |
| | - en |
| | tags: |
| | - ColBERT |
| | - PyLate |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:640000 |
| | - loss:Distillation |
| | base_model: NeuML/bert-hash-pico |
| | datasets: |
| | - lightonai/ms-marco-en-bge-gemma |
| | pipeline_tag: sentence-similarity |
| | library_name: PyLate |
| | license: apache-2.0 |
| | metrics: |
| | - MaxSim_accuracy@1 |
| | - MaxSim_accuracy@3 |
| | - MaxSim_accuracy@5 |
| | - MaxSim_accuracy@10 |
| | - MaxSim_precision@1 |
| | - MaxSim_precision@3 |
| | - MaxSim_precision@5 |
| | - MaxSim_precision@10 |
| | - MaxSim_recall@1 |
| | - MaxSim_recall@3 |
| | - MaxSim_recall@5 |
| | - MaxSim_recall@10 |
| | - MaxSim_ndcg@10 |
| | - MaxSim_mrr@10 |
| | - MaxSim_map@100 |
| | model-index: |
| | - name: ColBERT MUVERA Pico |
| | results: |
| | - task: |
| | type: py-late-information-retrieval |
| | name: Py Late Information Retrieval |
| | dataset: |
| | name: NanoClimateFEVER |
| | type: NanoClimateFEVER |
| | metrics: |
| | - type: MaxSim_accuracy@1 |
| | value: 0.22 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.32 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.4 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.54 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.22 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.11333333333333333 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.09200000000000001 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.062 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.125 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.18 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.22 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.2723333333333333 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.22523375350232466 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.2918015873015873 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.17799142530014567 |
| | name: Maxsim Map@100 |
| | - task: |
| | type: py-late-information-retrieval |
| | name: Py Late Information Retrieval |
| | dataset: |
| | name: NanoDBPedia |
| | type: NanoDBPedia |
| | metrics: |
| | - type: MaxSim_accuracy@1 |
| | value: 0.68 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.82 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.86 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.9 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.68 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.5733333333333333 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.49999999999999994 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.47 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.054334623940057496 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.13885343313592602 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.19014904910202987 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.32073944983710506 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.5579484125370805 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.7584126984126983 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.42613646438564345 |
| | name: Maxsim Map@100 |
| | - task: |
| | type: py-late-information-retrieval |
| | name: Py Late Information Retrieval |
| | dataset: |
| | name: NanoFEVER |
| | type: NanoFEVER |
| | metrics: |
| | - type: MaxSim_accuracy@1 |
| | value: 0.68 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.78 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.88 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.92 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.68 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.27333333333333326 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.19199999999999995 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.1 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.6466666666666667 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.7633333333333333 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.87 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.91 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.7844876010568872 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.7531666666666667 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.7413336663336665 |
| | name: Maxsim Map@100 |
| | - task: |
| | type: py-late-information-retrieval |
| | name: Py Late Information Retrieval |
| | dataset: |
| | name: NanoFiQA2018 |
| | type: NanoFiQA2018 |
| | metrics: |
| | - type: MaxSim_accuracy@1 |
| | value: 0.3 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.48 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.54 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.66 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.3 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.2 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.156 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.098 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.13885714285714287 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.28949206349206347 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.34890476190476183 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.4669047619047619 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.35173862968285535 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.41173809523809524 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.27418803367117023 |
| | name: Maxsim Map@100 |
| | - task: |
| | type: py-late-information-retrieval |
| | name: Py Late Information Retrieval |
| | dataset: |
| | name: NanoHotpotQA |
| | type: NanoHotpotQA |
| | metrics: |
| | - type: MaxSim_accuracy@1 |
| | value: 0.84 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.92 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.96 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.96 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.84 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.44666666666666655 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.288 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.152 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.42 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.67 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.72 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.76 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.7396469666031694 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.8846666666666667 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.6745663269195332 |
| | name: Maxsim Map@100 |
| | - task: |
| | type: py-late-information-retrieval |
| | name: Py Late Information Retrieval |
| | dataset: |
| | name: NanoMSMARCO |
| | type: NanoMSMARCO |
| | metrics: |
| | - type: MaxSim_accuracy@1 |
| | value: 0.4 |
| | 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: 0.72 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.4 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.18666666666666668 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.128 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.07200000000000001 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.4 |
| | 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: 0.72 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.5513379104118443 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.4978571428571428 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.5100075204701912 |
| | name: Maxsim Map@100 |
| | - task: |
| | type: py-late-information-retrieval |
| | name: Py Late Information Retrieval |
| | dataset: |
| | name: NanoNFCorpus |
| | type: NanoNFCorpus |
| | metrics: |
| | - type: MaxSim_accuracy@1 |
| | value: 0.42 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.5 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.58 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.62 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.42 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.36 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.316 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.256 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.040942245985757866 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.07446981664033472 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.08961908265974948 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.11750646414430962 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.31817112092123645 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.4845555555555556 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.12896096534086632 |
| | 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: 0.34 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.62 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.66 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.72 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.34 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.20666666666666664 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.132 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.07400000000000001 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.32 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.58 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.62 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.68 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.5190287199365009 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.4836031746031745 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.4681591756850757 |
| | name: Maxsim Map@100 |
| | - task: |
| | type: py-late-information-retrieval |
| | name: Py Late Information Retrieval |
| | dataset: |
| | name: NanoQuoraRetrieval |
| | type: NanoQuoraRetrieval |
| | metrics: |
| | - type: MaxSim_accuracy@1 |
| | value: 0.82 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.9 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.9 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.92 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.82 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.32666666666666666 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.20799999999999996 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.11199999999999999 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.7440000000000001 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.8306666666666667 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.8540000000000001 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.8773333333333332 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.8467657844266899 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.8625 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.8312680912657447 |
| | 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: 0.32 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.52 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.58 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.66 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.32 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.21333333333333332 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.18 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.12 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.066 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.13366666666666668 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.18566666666666662 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.24566666666666667 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.2462853863944484 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.4382222222222222 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.17492730667100076 |
| | 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: 0.1 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.3 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.4 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.46 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.1 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.09999999999999998 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.08000000000000002 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.046000000000000006 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.1 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.3 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.4 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.46 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.275284156147708 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.21590476190476193 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.22481590517812997 |
| | 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: 0.48 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.64 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.72 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.78 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.48 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.22666666666666668 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.15999999999999998 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.088 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.445 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.62 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.71 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.78 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.6190838299940942 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.5744444444444444 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.5693936722581301 |
| | 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: 1.0 |
| | 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: 0.6462585034013605 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.616326530612245 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.4938775510204082 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.04572557745748646 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.12524012746937077 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.19598845564624598 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.3058088560800141 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.5544751618654643 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.7962585034013605 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.37144809611649504 |
| | name: Maxsim Map@100 |
| | - task: |
| | type: nano-beir |
| | name: Nano BEIR |
| | dataset: |
| | name: NanoBEIR mean |
| | type: NanoBEIR_mean |
| | metrics: |
| | - type: MaxSim_accuracy@1 |
| | value: 0.4825745682888539 |
| | name: Maxsim Accuracy@1 |
| | - type: MaxSim_accuracy@3 |
| | value: 0.6367974882260596 |
| | name: Maxsim Accuracy@3 |
| | - type: MaxSim_accuracy@5 |
| | value: 0.7015384615384617 |
| | name: Maxsim Accuracy@5 |
| | - type: MaxSim_accuracy@10 |
| | value: 0.7584615384615384 |
| | name: Maxsim Accuracy@10 |
| | - type: MaxSim_precision@1 |
| | value: 0.4825745682888539 |
| | name: Maxsim Precision@1 |
| | - type: MaxSim_precision@3 |
| | value: 0.2979173207744636 |
| | name: Maxsim Precision@3 |
| | - type: MaxSim_precision@5 |
| | value: 0.23448665620094195 |
| | name: Maxsim Precision@5 |
| | - type: MaxSim_precision@10 |
| | value: 0.16491365777080064 |
| | name: Maxsim Precision@10 |
| | - type: MaxSim_recall@1 |
| | value: 0.2728097120697778 |
| | name: Maxsim Recall@1 |
| | - type: MaxSim_recall@3 |
| | value: 0.4050555467234125 |
| | name: Maxsim Recall@3 |
| | - type: MaxSim_recall@5 |
| | value: 0.4649483089214964 |
| | name: Maxsim Recall@5 |
| | - type: MaxSim_recall@10 |
| | value: 0.5320225280999634 |
| | name: Maxsim Recall@10 |
| | - type: MaxSim_ndcg@10 |
| | value: 0.5068836487292541 |
| | name: Maxsim Ndcg@10 |
| | - type: MaxSim_mrr@10 |
| | value: 0.573317809174952 |
| | name: Maxsim Mrr@10 |
| | - type: MaxSim_map@100 |
| | value: 0.4287074345842918 |
| | name: Maxsim Map@100 |
| | --- |
| | |
| | # ColBERT MUVERA Pico |
| |
|
| | This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [neuml/bert-hash-pico](https://huggingface.co/neuml/bert-hash-pico) on the [msmarco-en-bge-gemma unnormalized split](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma) dataset. It maps sentences & paragraphs to sequences of 80-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-pico", |
| | 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-pico", 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-pico", |
| | ) |
| | |
| | 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: BertHashModel |
| | (1): Dense({'in_features': 80, '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 | NFCorpus | SciDocs | SciFact | Average | |
| | |:------------------|:-----------|:---------|:---------|:--------|:--------| |
| | | [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.3165 | 0.1497 | 0.6456 | 0.3706 | |
| | | [ColBERT MUVERA Femto](https://huggingface.co/neuml/colbert-muvera-femto) | 0.2M | 0.2513 | 0.0870 | 0.4710 | 0.2698 | |
| | | [**ColBERT MUVERA Pico**](https://huggingface.co/neuml/colbert-muvera-pico) | **0.4M** | **0.3005** | **0.1117** | **0.6452** | **0.3525** | |
| | | [ColBERT MUVERA Nano](https://huggingface.co/neuml/colbert-muvera-nano) | 0.9M | 0.3180 | 0.1262 | 0.6576 | 0.3673 | |
| | | [ColBERT MUVERA Micro](https://huggingface.co/neuml/colbert-muvera-micro) | 4M | 0.3235 | 0.1244 | 0.6676 | 0.3718 | |
| |
|
| | #### MUVERA encoding + maxsim re-ranking of the top 100 results per MUVERA paper |
| |
|
| | | Model | Parameters | NFCorpus | SciDocs | SciFact | Average | |
| | |:------------------|:-----------|:---------|:---------|:--------|:--------| |
| | | [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.3025 | 0.1538 | 0.6278 | 0.3614 | |
| | | [ColBERT MUVERA Femto](https://huggingface.co/neuml/colbert-muvera-femto) | 0.2M | 0.2316 | 0.0858 | 0.4641 | 0.2605 | |
| | | [**ColBERT MUVERA Pico**](https://huggingface.co/neuml/colbert-muvera-pico) | **0.4M** | **0.2821** | **0.1004** | **0.6090** | **0.3305** | |
| | | [ColBERT MUVERA Nano](https://huggingface.co/neuml/colbert-muvera-nano) | 0.9M | 0.2996 | 0.1201 | 0.6249 | 0.3482 | |
| | | [ColBERT MUVERA Micro](https://huggingface.co/neuml/colbert-muvera-micro) | 4M | 0.3095 | 0.1228 | 0.6464 | 0.3596 | |
| |
|
| | #### MUVERA encoding only |
| |
|
| | | Model | Parameters | NFCorpus | SciDocs | SciFact | Average | |
| | |:------------------|:-----------|:---------|:---------|:--------|:--------| |
| | | [ColBERT v2](https://huggingface.co/colbert-ir/colbertv2.0) | 110M | 0.2356 | 0.1229 | 0.5002 | 0.2862 | |
| | | [ColBERT MUVERA Femto](https://huggingface.co/neuml/colbert-muvera-femto) | 0.2M | 0.1851 | 0.0411 | 0.3518 | 0.1927 | |
| | | [**ColBERT MUVERA Pico**](https://huggingface.co/neuml/colbert-muvera-pico) | **0.4M** | **0.1926** | **0.0564** | **0.4424** | **0.2305** | |
| | | [ColBERT MUVERA Nano](https://huggingface.co/neuml/colbert-muvera-nano) | 0.9M | 0.2355 | 0.0807 | 0.4904 | 0.2689 | |
| | | [ColBERT MUVERA Micro](https://huggingface.co/neuml/colbert-muvera-micro) | 4M | 0.2348 | 0.0882 | 0.4875 | 0.2702 | |
| |
|
| | _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. |
| |
|
| | **At 450K parameters, this model does shockingly well! It's not too far off from the baseline 4M parameter model at 1/10th the size. It's also not too far off from the original ColBERT v2 model, which has 110M parameters.** |
| |
|
| | ### Nano BEIR |
| | * Dataset: `NanoBEIR_mean` |
| | * Evaluated with <code>pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator</code> |
| |
|
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | MaxSim_accuracy@1 | 0.4826 | |
| | | MaxSim_accuracy@3 | 0.6368 | |
| | | MaxSim_accuracy@5 | 0.7015 | |
| | | MaxSim_accuracy@10 | 0.7585 | |
| | | MaxSim_precision@1 | 0.4826 | |
| | | MaxSim_precision@3 | 0.2979 | |
| | | MaxSim_precision@5 | 0.2345 | |
| | | MaxSim_precision@10 | 0.1649 | |
| | | MaxSim_recall@1 | 0.2728 | |
| | | MaxSim_recall@3 | 0.4051 | |
| | | MaxSim_recall@5 | 0.4649 | |
| | | MaxSim_recall@10 | 0.532 | |
| | | **MaxSim_ndcg@10** | **0.5069** | |
| | | MaxSim_mrr@10 | 0.5733 | |
| | | MaxSim_map@100 | 0.4287 | |
| | |
| | ## Training Details |
| | |
| | ### Training Hyperparameters |
| | |
| | #### Non-Default Hyperparameters |
| | |
| | - `eval_strategy`: steps |
| | - `per_device_train_batch_size`: 32 |
| | - `learning_rate`: 0.0003 |
| | - `num_train_epochs`: 1 |
| | - `warmup_ratio`: 0.05 |
| | - `fp16`: True |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: steps |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 32 |
| | - `per_device_eval_batch_size`: 8 |
| | - `per_gpu_train_batch_size`: None |
| | - `per_gpu_eval_batch_size`: None |
| | - `gradient_accumulation_steps`: 1 |
| | - `eval_accumulation_steps`: None |
| | - `torch_empty_cache_steps`: None |
| | - `learning_rate`: 0.0003 |
| | - `weight_decay`: 0.0 |
| | - `adam_beta1`: 0.9 |
| | - `adam_beta2`: 0.999 |
| | - `adam_epsilon`: 1e-08 |
| | - `max_grad_norm`: 1.0 |
| | - `num_train_epochs`: 1 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: {} |
| | - `warmup_ratio`: 0.05 |
| | - `warmup_steps`: 0 |
| | - `log_level`: passive |
| | - `log_level_replica`: warning |
| | - `log_on_each_node`: True |
| | - `logging_nan_inf_filter`: True |
| | - `save_safetensors`: True |
| | - `save_on_each_node`: False |
| | - `save_only_model`: False |
| | - `restore_callback_states_from_checkpoint`: False |
| | - `no_cuda`: False |
| | - `use_cpu`: False |
| | - `use_mps_device`: False |
| | - `seed`: 42 |
| | - `data_seed`: None |
| | - `jit_mode_eval`: False |
| | - `bf16`: False |
| | - `fp16`: True |
| | - `fp16_opt_level`: O1 |
| | - `half_precision_backend`: auto |
| | - `bf16_full_eval`: False |
| | - `fp16_full_eval`: False |
| | - `tf32`: None |
| | - `local_rank`: 0 |
| | - `ddp_backend`: None |
| | - `tpu_num_cores`: None |
| | - `tpu_metrics_debug`: False |
| | - `debug`: [] |
| | - `dataloader_drop_last`: False |
| | - `dataloader_num_workers`: 0 |
| | - `dataloader_prefetch_factor`: None |
| | - `past_index`: -1 |
| | - `disable_tqdm`: False |
| | - `remove_unused_columns`: True |
| | - `label_names`: None |
| | - `load_best_model_at_end`: False |
| | - `ignore_data_skip`: False |
| | - `fsdp`: [] |
| | - `fsdp_min_num_params`: 0 |
| | - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| | - `fsdp_transformer_layer_cls_to_wrap`: None |
| | - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| | - `parallelism_config`: None |
| | - `deepspeed`: None |
| | - `label_smoothing_factor`: 0.0 |
| | - `optim`: adamw_torch_fused |
| | - `optim_args`: None |
| | - `adafactor`: False |
| | - `group_by_length`: False |
| | - `length_column_name`: length |
| | - `project`: huggingface |
| | - `trackio_space_id`: trackio |
| | - `ddp_find_unused_parameters`: None |
| | - `ddp_bucket_cap_mb`: None |
| | - `ddp_broadcast_buffers`: False |
| | - `dataloader_pin_memory`: True |
| | - `dataloader_persistent_workers`: False |
| | - `skip_memory_metrics`: True |
| | - `use_legacy_prediction_loop`: False |
| | - `push_to_hub`: False |
| | - `resume_from_checkpoint`: None |
| | - `hub_model_id`: None |
| | - `hub_strategy`: every_save |
| | - `hub_private_repo`: None |
| | - `hub_always_push`: False |
| | - `hub_revision`: None |
| | - `gradient_checkpointing`: False |
| | - `gradient_checkpointing_kwargs`: None |
| | - `include_inputs_for_metrics`: False |
| | - `include_for_metrics`: [] |
| | - `eval_do_concat_batches`: True |
| | - `fp16_backend`: auto |
| | - `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 |
| | - `include_tokens_per_second`: False |
| | - `include_num_input_tokens_seen`: no |
| | - `neftune_noise_alpha`: None |
| | - `optim_target_modules`: None |
| | - `batch_eval_metrics`: False |
| | - `eval_on_start`: False |
| | - `use_liger_kernel`: False |
| | - `liger_kernel_config`: None |
| | - `eval_use_gather_object`: False |
| | - `average_tokens_across_devices`: True |
| | - `prompts`: None |
| | - `batch_sampler`: batch_sampler |
| | - `multi_dataset_batch_sampler`: proportional |
| | |
| | </details> |
| | |
| | ### Framework Versions |
| | - Python: 3.10.18 |
| | - Sentence Transformers: 4.0.2 |
| | - PyLate: 1.3.2 |
| | - Transformers: 4.57.0 |
| | - PyTorch: 2.8.0+cu128 |
| | - Accelerate: 1.10.1 |
| | - Datasets: 4.1.1 |
| | - Tokenizers: 0.22.1 |
| | |
| | ## 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} |
| | } |
| | ``` |
| | |
