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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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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6300 |
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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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widget: |
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- source_sentence: Balance as of December 31, 2023 for Medicaid and Medicare Rebates |
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was $5,297 million, for Managed Care Rebates was $7,020 million, and for Wholesaler |
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Chargebacks was $1,172 million. |
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sentences: |
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- What can Membership Rewards points be redeemed for? |
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- What were the ending balances for Medicaid and Medicare Rebates, Managed Care |
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Rebates, and Wholesaler Chargebacks as of December 31, 2023? |
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- What was the percentage increase in the general and administrative expenses from |
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the fiscal year ending on October 2, 2022, to the fiscal year ending on October |
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1, 2023? |
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- source_sentence: In analyzing goodwill for potential impairment in the quantitative |
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impairment test, the company uses the market approach, when available and appropriate, |
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or a combination of the income and market approaches to estimate the reporting |
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unit’s fair value. |
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sentences: |
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- What is the purpose of Visa according to the overview provided? |
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- What approaches does the company use to analyze goodwill for potential impairment |
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in the quantitative impairment test? |
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- What method is used to record amortization and costs for owned content that is |
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predominantly monetized on an individual basis? |
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- source_sentence: This report includes forward-looking statements within the meaning |
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of the Private Securities Litigation Reform Act of 1995, which are subject to |
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risks and uncertainties. |
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sentences: |
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- What are forward-looking statements in financial reports? |
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- What percentage of the Pharmacy & Consumer Wellness segment's revenues did the |
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pharmacy category constitute in 2023? |
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- What are the depreciation methods and useful life estimates for buildings, furniture, |
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and computer equipment as mentioned in the company's accounting policies? |
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- source_sentence: We would use the net proceeds from the sale of any securities offered |
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pursuant to the shelf registration statement for general corporate purposes, which |
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may include funding for working capital, financing capital expenditures, research |
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and development, and potential acquisitions or strategic alliances. |
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sentences: |
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- What measures does Goldman Sachs employ to handle their cyber incident response? |
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- What awards did the company receive in 2022 for environmental and safety achievements? |
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- How are the proceeds from the shelf registration statement planned to be used? |
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- source_sentence: We use a variety of practices to measure and support progress against |
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these growth behaviors and to ensure that our employees are engaged and fulfilled |
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at work. |
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sentences: |
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- How does the company measure and support employee engagement and cultural growth? |
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- How does the company's membership format affect its profitability? |
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- What is the maximum additional exclusivity period granted by the FDA for approved |
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drugs that undergo pediatric testing? |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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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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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: 0.7071428571428572 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8314285714285714 |
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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: 0.9228571428571428 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7071428571428572 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27714285714285714 |
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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: 0.09228571428571428 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7071428571428572 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8314285714285714 |
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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: 0.9228571428571428 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8152573597721203 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7808815192743759 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7835857411528796 |
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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: 0.6971428571428572 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8328571428571429 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8742857142857143 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9157142857142857 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.6971428571428572 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2776190476190476 |
|
name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17485714285714285 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09157142857142857 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.6971428571428572 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8328571428571429 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8742857142857143 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9157142857142857 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8089182108201057 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7743531746031744 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.777472809187461 |
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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: 0.6957142857142857 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.83 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.87 |
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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: 0.6957142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
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value: 0.27666666666666667 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
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value: 0.174 |
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name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
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value: 0.09099999999999998 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
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value: 0.6957142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.83 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.87 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
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value: 0.91 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8052344976922489 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7713877551020404 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7749003964653882 |
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name: Cosine Map@100 |
|
- 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: 0.6828571428571428 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8257142857142857 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8528571428571429 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9071428571428571 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6828571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2752380952380953 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17057142857142854 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09071428571428569 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6828571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8257142857142857 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8528571428571429 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9071428571428571 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7972100056891113 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7619444444444445 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7654665230481205 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
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type: dim_64 |
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metrics: |
|
- type: cosine_accuracy@1 |
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value: 0.6371428571428571 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8042857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8428571428571429 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8814285714285715 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6371428571428571 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2680952380952381 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16857142857142854 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08814285714285712 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6371428571428571 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8042857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8428571428571429 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8814285714285715 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7645594630559873 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7265028344671197 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7306525198080603 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base Financial Matryoshka |
|
|
|
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) on the json dataset. 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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## Model Details |
|
|
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### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- json |
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- **Language:** en |
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- **License:** apache-2.0 |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
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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``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("mogmix/bge-base-financial-matryoshka") |
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# Run inference |
|
sentences = [ |
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'We use a variety of practices to measure and support progress against these growth behaviors and to ensure that our employees are engaged and fulfilled at work.', |
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'How does the company measure and support employee engagement and cultural growth?', |
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"How does the company's membership format affect its profitability?", |
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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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|
|
# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
|
# [3, 3] |
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``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
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|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### 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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|
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<details><summary>Click to expand</summary> |
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|
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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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|
|
*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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## Evaluation |
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|
|
### Metrics |
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|
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#### Information Retrieval |
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|
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* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `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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|
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| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
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|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.7071 | 0.6971 | 0.6957 | 0.6829 | 0.6371 | |
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| cosine_accuracy@3 | 0.8314 | 0.8329 | 0.83 | 0.8257 | 0.8043 | |
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| cosine_accuracy@5 | 0.8729 | 0.8743 | 0.87 | 0.8529 | 0.8429 | |
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| cosine_accuracy@10 | 0.9229 | 0.9157 | 0.91 | 0.9071 | 0.8814 | |
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| cosine_precision@1 | 0.7071 | 0.6971 | 0.6957 | 0.6829 | 0.6371 | |
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| cosine_precision@3 | 0.2771 | 0.2776 | 0.2767 | 0.2752 | 0.2681 | |
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| cosine_precision@5 | 0.1746 | 0.1749 | 0.174 | 0.1706 | 0.1686 | |
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| cosine_precision@10 | 0.0923 | 0.0916 | 0.091 | 0.0907 | 0.0881 | |
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| cosine_recall@1 | 0.7071 | 0.6971 | 0.6957 | 0.6829 | 0.6371 | |
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| cosine_recall@3 | 0.8314 | 0.8329 | 0.83 | 0.8257 | 0.8043 | |
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| cosine_recall@5 | 0.8729 | 0.8743 | 0.87 | 0.8529 | 0.8429 | |
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| cosine_recall@10 | 0.9229 | 0.9157 | 0.91 | 0.9071 | 0.8814 | |
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| **cosine_ndcg@10** | **0.8153** | **0.8089** | **0.8052** | **0.7972** | **0.7646** | |
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| cosine_mrr@10 | 0.7809 | 0.7744 | 0.7714 | 0.7619 | 0.7265 | |
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| cosine_map@100 | 0.7836 | 0.7775 | 0.7749 | 0.7655 | 0.7307 | |
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|
|
<!-- |
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## Bias, Risks and Limitations |
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|
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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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--> |
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|
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<!-- |
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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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--> |
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|
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## Training Details |
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|
|
### Training Dataset |
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|
|
#### json |
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|
|
* Dataset: json |
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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: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 45.46 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.55 tokens</li><li>max: 41 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| |
|
| <code>We believe our residential connectivity revenue will increase as a result of growth in average domestic broadband revenue per customer, as well as increases in domestic wireless and international connectivity revenue.</code> | <code>What are the projected trends for Comcast's residential connectivity revenue in 2023?</code> | |
|
| <code>The company's Artificial Intelligence Platform (AIP) leverages machine learning technologies and LLMs within the Gotham and Foundry platforms to connect AI with enterprise data, aiding in decision-making processes.</code> | <code>How does the company integrate large language models with its software platforms?</code> | |
|
| <code>The impairment charges for Depop and Elo7 were influenced by factors such as macroeconomic conditions including reopening and inflation, as well as management changes and revised projected cash flows affecting their fair values.</code> | <code>What factors contributed to the impairment charges for Depop and Elo7 in 2022?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
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{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
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], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `gradient_accumulation_steps`: 16 |
|
- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 4 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: True |
|
- `load_best_model_at_end`: True |
|
- `optim`: adamw_torch_fused |
|
- `batch_sampler`: no_duplicates |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `torch_empty_cache_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `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 |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: True |
|
- `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`: True |
|
- `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} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `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 |
|
- `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 |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.8122 | 10 | 1.5675 | - | - | - | - | - | |
|
| 1.0 | 13 | - | 0.8000 | 0.7975 | 0.7897 | 0.7811 | 0.7419 | |
|
| 1.5685 | 20 | 0.6203 | - | - | - | - | - | |
|
| 2.0 | 26 | - | 0.8114 | 0.8063 | 0.8044 | 0.7928 | 0.7599 | |
|
| 2.3249 | 30 | 0.4678 | - | - | - | - | - | |
|
| 3.0 | 39 | - | 0.8152 | 0.8092 | 0.8046 | 0.7967 | 0.7660 | |
|
| 3.0812 | 40 | 0.4106 | - | - | - | - | - | |
|
| **3.731** | **48** | **-** | **0.8153** | **0.8089** | **0.8052** | **0.7972** | **0.7646** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.12.7 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.47.0 |
|
- PyTorch: 2.5.1+cu124 |
|
- Accelerate: 1.2.1 |
|
- Datasets: 3.2.0 |
|
- Tokenizers: 0.21.0 |
|
|
|
## 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", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
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}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
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}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
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