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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- dataset_size:1K<n<10K |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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widget: |
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- source_sentence: What service does Walmart GoLocal provide? |
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sentences: |
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- What services does Walmart Connect offer? |
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- What is the process for using reinsurers not on the authorized list? |
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- What is the principal amount of debt maturing in fiscal year 2023? |
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- source_sentence: What is the focus of DaVita Venture Group? |
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sentences: |
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- What is the main business focus of Eli Lilly and Company? |
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- By what percentage did AbbVie's Skyrizi net revenues increase in 2023? |
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- What were the net catastrophe losses in U.S. dollars in 2023? |
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- source_sentence: What are Kroger’s four strategic pillars? |
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sentences: |
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- What is the nature of Kroger's business operations? |
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- What interest rates are applicable to the notes issued in April 2022? |
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- Proceeds from issuance of long-term debt in 2023 amounted to $872.9 million. |
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- source_sentence: What was the effective tax rate in 2023? |
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sentences: |
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- What was the effective income tax rate for the Company in 2023? |
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- What major restructuring activities were completed by the end of 2023? |
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- What are the primary responsibilities of Chubb's Product Boards? |
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- source_sentence: The return on equity for 2023 was 27.0%. |
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sentences: |
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- What was the return on equity for 2023? |
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- What was the total net property and equipment as of December 31, 2023? |
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- How does CARB enforce its ZEV mandates and what consequence faces non-compliance? |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7028571428571428 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.84 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8828571428571429 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9214285714285714 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7028571428571428 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17657142857142855 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09214285714285714 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7028571428571428 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.84 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8828571428571429 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9214285714285714 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8149004529112371 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7804058956916099 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7839215377734133 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7028571428571428 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8385714285714285 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8871428571428571 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9257142857142857 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7028571428571428 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2795238095238095 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1774285714285714 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09257142857142854 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7028571428571428 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8385714285714285 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8871428571428571 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9257142857142857 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8152847434616775 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7796893424036281 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7829824429897414 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7057142857142857 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8328571428571429 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8728571428571429 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9114285714285715 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7057142857142857 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2776190476190476 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17457142857142854 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09114285714285714 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7057142857142857 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8328571428571429 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8728571428571429 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9114285714285715 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8097167926128003 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7770238095238095 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7810667608834199 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.68 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8285714285714286 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8671428571428571 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.91 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.68 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27619047619047615 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
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value: 0.1734285714285714 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.091 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.68 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8285714285714286 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8671428571428571 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.91 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7972014326060813 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.760804988662131 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7644398940079736 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 64 |
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type: dim_64 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.6614285714285715 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.7942857142857143 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.83 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8757142857142857 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.6614285714285715 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
|
value: 0.26476190476190475 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16599999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08757142857142856 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
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value: 0.6614285714285715 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7942857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.83 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8757142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7702103944866356 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7363231292517006 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7414139881512513 |
|
name: Cosine Map@100 |
|
--- |
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|
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# BGE base Financial Matryoshka |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
|
|
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### Model Sources |
|
|
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
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### Full Model Architecture |
|
|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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|
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("uonyeka/bge-base-financial-matryoshka") |
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# Run inference |
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sentences = [ |
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'The return on equity for 2023 was 27.0%.', |
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'What was the return on equity for 2023?', |
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'What was the total net property and equipment as of December 31, 2023?', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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|
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<!-- |
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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|
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Information Retrieval |
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* Dataset: `dim_768` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.7029 | |
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| cosine_accuracy@3 | 0.84 | |
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| cosine_accuracy@5 | 0.8829 | |
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| cosine_accuracy@10 | 0.9214 | |
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| cosine_precision@1 | 0.7029 | |
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| cosine_precision@3 | 0.28 | |
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| cosine_precision@5 | 0.1766 | |
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| cosine_precision@10 | 0.0921 | |
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| cosine_recall@1 | 0.7029 | |
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| cosine_recall@3 | 0.84 | |
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| cosine_recall@5 | 0.8829 | |
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| cosine_recall@10 | 0.9214 | |
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| cosine_ndcg@10 | 0.8149 | |
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| cosine_mrr@10 | 0.7804 | |
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| **cosine_map@100** | **0.7839** | |
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|
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#### Information Retrieval |
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* Dataset: `dim_512` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:----------| |
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| cosine_accuracy@1 | 0.7029 | |
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| cosine_accuracy@3 | 0.8386 | |
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| cosine_accuracy@5 | 0.8871 | |
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| cosine_accuracy@10 | 0.9257 | |
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| cosine_precision@1 | 0.7029 | |
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| cosine_precision@3 | 0.2795 | |
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| cosine_precision@5 | 0.1774 | |
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| cosine_precision@10 | 0.0926 | |
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| cosine_recall@1 | 0.7029 | |
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| cosine_recall@3 | 0.8386 | |
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| cosine_recall@5 | 0.8871 | |
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| cosine_recall@10 | 0.9257 | |
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| cosine_ndcg@10 | 0.8153 | |
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| cosine_mrr@10 | 0.7797 | |
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| **cosine_map@100** | **0.783** | |
|
|
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#### Information Retrieval |
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* Dataset: `dim_256` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.7057 | |
|
| cosine_accuracy@3 | 0.8329 | |
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| cosine_accuracy@5 | 0.8729 | |
|
| cosine_accuracy@10 | 0.9114 | |
|
| cosine_precision@1 | 0.7057 | |
|
| cosine_precision@3 | 0.2776 | |
|
| cosine_precision@5 | 0.1746 | |
|
| cosine_precision@10 | 0.0911 | |
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| cosine_recall@1 | 0.7057 | |
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| cosine_recall@3 | 0.8329 | |
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| cosine_recall@5 | 0.8729 | |
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| cosine_recall@10 | 0.9114 | |
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| cosine_ndcg@10 | 0.8097 | |
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| cosine_mrr@10 | 0.777 | |
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| **cosine_map@100** | **0.7811** | |
|
|
|
#### Information Retrieval |
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* Dataset: `dim_128` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
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| Metric | Value | |
|
|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.68 | |
|
| cosine_accuracy@3 | 0.8286 | |
|
| cosine_accuracy@5 | 0.8671 | |
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| cosine_accuracy@10 | 0.91 | |
|
| cosine_precision@1 | 0.68 | |
|
| cosine_precision@3 | 0.2762 | |
|
| cosine_precision@5 | 0.1734 | |
|
| cosine_precision@10 | 0.091 | |
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| cosine_recall@1 | 0.68 | |
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| cosine_recall@3 | 0.8286 | |
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| cosine_recall@5 | 0.8671 | |
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| cosine_recall@10 | 0.91 | |
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| cosine_ndcg@10 | 0.7972 | |
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| cosine_mrr@10 | 0.7608 | |
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| **cosine_map@100** | **0.7644** | |
|
|
|
#### Information Retrieval |
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* Dataset: `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.6614 | |
|
| cosine_accuracy@3 | 0.7943 | |
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| cosine_accuracy@5 | 0.83 | |
|
| cosine_accuracy@10 | 0.8757 | |
|
| cosine_precision@1 | 0.6614 | |
|
| cosine_precision@3 | 0.2648 | |
|
| cosine_precision@5 | 0.166 | |
|
| cosine_precision@10 | 0.0876 | |
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| cosine_recall@1 | 0.6614 | |
|
| cosine_recall@3 | 0.7943 | |
|
| cosine_recall@5 | 0.83 | |
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| cosine_recall@10 | 0.8757 | |
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| cosine_ndcg@10 | 0.7702 | |
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| cosine_mrr@10 | 0.7363 | |
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| **cosine_map@100** | **0.7414** | |
|
|
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 6,300 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | positive | anchor | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 45.2 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.41 tokens</li><li>max: 43 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------| |
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| <code>The cash equities rate per contract (per 100 shares) for NYSE increased by 6%, from $0.045 in 2022 to $0.048 in 2023.</code> | <code>What was the change in the rate per contract for NYSE cash equities from 2022 to 2023?</code> | |
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| <code>Item 3 specifies that the information regarding Legal Proceedings is sourced from Note 19 of the Notes to Consolidated Financial Statements included in Item 8.</code> | <code>What is the content source for the information requested by Item 3 concerning Legal Proceedings?</code> | |
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| <code>North America's operating income for the fiscal year ended October 1, 2023, was $5,495.7 million, up from $4,486.5 million in fiscal 2022.</code> | <code>What was the increase in North America's operating income from fiscal 2022 to fiscal 2023?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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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`: epoch |
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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`: 16 |
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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`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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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`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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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`: True |
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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`: True |
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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_fused |
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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`: False |
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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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- `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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- `dispatch_batches`: None |
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- `split_batches`: 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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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
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|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
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| 0.8122 | 10 | 1.575 | - | - | - | - | - | |
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| 0.9746 | 12 | - | 0.7437 | 0.7623 | 0.7682 | 0.7114 | 0.7652 | |
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| 1.6244 | 20 | 0.697 | - | - | - | - | - | |
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| 1.9492 | 24 | - | 0.7619 | 0.7760 | 0.7824 | 0.7346 | 0.7826 | |
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| 2.4365 | 30 | 0.4724 | - | - | - | - | - | |
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| 2.9239 | 36 | - | 0.7639 | 0.7808 | 0.7831 | 0.7398 | 0.7834 | |
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| 3.2487 | 40 | 0.3999 | - | - | - | - | - | |
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| **3.8985** | **48** | **-** | **0.7644** | **0.7811** | **0.783** | **0.7414** | **0.7839** | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.0 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.1.2+cu121 |
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- Accelerate: 0.30.1 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MatryoshkaLoss |
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```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
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year={2024}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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