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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: What is the anticipated total capital investment range for fiscal |
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year 2024 related to property and equipment? |
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sentences: |
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- Apollo, which we began offering as a commercial solution in 2021, is a cloud-agnostic, |
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single control layer that coordinates ongoing delivery of new features, security |
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updates, and platform configurations, helping to ensure the continuous operation |
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of critical systems. |
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- During fiscal year 2024, we expect to use our existing cash and cash equivalents, |
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our marketable securities, and the cash generated by our operations to fund our |
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capital investments of approximately $1.10 billion to $1.30 billion related to |
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property and equipment. |
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- Joseph F. Wayland was appointed as General Counsel and Secretary of Chubb Limited |
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in July 2013. |
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- source_sentence: What was the effective price per share of class A common stock |
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for fiscal 2023 under the U.S. retrospective responsibility plan? |
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sentences: |
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- We operated 692 gas stations at the end of 2023. |
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- The effective price per share for class A common stock under the U.S. retrospective |
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responsibility plan for fiscal 2023 was $221.33, calculated using the weighted-average |
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price based on the volume-weighted average price during the pricing period. |
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- For a description of our material pending legal proceedings, see Legal Matters |
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in Array 10 of the Notes to Consolidated Financial Statements included in Part |
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II, Item 8 of this Annual Report on... |
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- source_sentence: What financial challenge did the company face in 2017 and how did |
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it impact them legally? |
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sentences: |
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- In 2017, we experienced a material cybersecurity incident following a criminal |
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attack on our systems that involved the theft of personal information of consumers. |
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As a result of the 2017 cybersecurity incident, we were subject to proceedings |
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and investigations. |
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- The overall net effect on our gross margin from inventory provisions and sales |
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of items previously written down was an unfavorable impact of 7.5% in fiscal year |
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2023 and 0.9% in fiscal year 2022. |
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- The adjusted after-tax return on invested capital (ROIC) is computed by dividing |
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the after-tax operating profit, excluding rent expenses, by the total invested |
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capital which factors in the capitalization of operating leases. |
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- source_sentence: What is the title of Item 8 in the document? |
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sentences: |
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- Research and development expenses for fiscal year 2023 increased by $142 million |
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over the previous fiscal year. |
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- As of January 28, 2023, the company was authorized to issue up to 10,000,000 preferred |
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shares, each with a par value of $0.01. However, there were no preferred shares |
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issued and outstanding as of January 28, 2023 and the previous year as well. |
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- Item 8 of the document is titled 'Financial Statements and Supplementary Data'. |
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- source_sentence: How many shares were outstanding at the beginning of 2023 and what |
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was their aggregate intrinsic value? |
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sentences: |
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- At the beginning of 2023, there were 355 shares outstanding with an aggregate |
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intrinsic value of $142,916. |
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- In IBM’s 2023 Annual Report to Stockholders, the Financial Statements and Supplementary |
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Data are included on pages 44 through 121. |
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- Privacy and data protection regulations affect operations by dictating how data |
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is used and handled, impacting product offering and operation. |
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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: 0.7171428571428572 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8414285714285714 |
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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: 0.9071428571428571 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7171428571428572 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28047619047619043 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.174 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09071428571428569 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7171428571428572 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8414285714285714 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.87 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9071428571428571 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8148141512407042 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7849903628117916 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7887661106132665 |
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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: 0.7171428571428572 |
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|
name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8471428571428572 |
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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: 0.9085714285714286 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7171428571428572 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28238095238095234 |
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name: Cosine Precision@3 |
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- 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: 0.09085714285714284 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7171428571428572 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8471428571428572 |
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|
name: Cosine Recall@3 |
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|
- type: cosine_recall@5 |
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value: 0.8671428571428571 |
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|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9085714285714286 |
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|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8146547679133024 |
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|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7843815192743763 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7879908541996482 |
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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: 0.7171428571428572 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8414285714285714 |
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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: 0.9014285714285715 |
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|
name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7171428571428572 |
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|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28047619047619043 |
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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: 0.09014285714285714 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7171428571428572 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8414285714285714 |
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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: 0.9014285714285715 |
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|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.81391710609103 |
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|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
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value: 0.7854092970521545 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
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value: 0.7894965954567308 |
|
|
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: 0.7128571428571429 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.8242857142857143 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.8585714285714285 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.9028571428571428 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.7128571428571429 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.2747619047619047 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.1717142857142857 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.09028571428571427 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.7128571428571429 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.8242857142857143 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.8585714285714285 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.9028571428571428 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.8071617730563218 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
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value: 0.7765963718820862 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.7803238233984962 |
|
|
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 64 |
|
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type: dim_64 |
|
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metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.6914285714285714 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.7942857142857143 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.8314285714285714 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.8728571428571429 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.6914285714285714 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.26476190476190475 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.16628571428571426 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.08728571428571427 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.6914285714285714 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.7942857142857143 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.8314285714285714 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.8728571428571429 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.7805313652937041 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.7512346938775507 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.7553555070551027 |
|
|
name: Cosine Map@100 |
|
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--- |
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# BGE base Financial Matryoshka |
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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) 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 |
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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 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 |
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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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|
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### Full Model Architecture |
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|
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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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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
|
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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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# Download from the 🤗 Hub |
|
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model = SentenceTransformer("phucvt0302/bge-base-financial-matryoshka") |
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# Run inference |
|
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sentences = [ |
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'How many shares were outstanding at the beginning of 2023 and what was their aggregate intrinsic value?', |
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'At the beginning of 2023, there were 355 shares outstanding with an aggregate intrinsic value of $142,916.', |
|
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'In IBM’s 2023 Annual Report to Stockholders, the Financial Statements and Supplementary Data are included on pages 44 through 121.', |
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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) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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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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### Downstream Usage (Sentence Transformers) |
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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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### 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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#### 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) with these parameters: |
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```json |
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{ |
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"truncate_dim": 768 |
|
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} |
|
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``` |
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|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7171 | |
|
|
| cosine_accuracy@3 | 0.8414 | |
|
|
| cosine_accuracy@5 | 0.87 | |
|
|
| cosine_accuracy@10 | 0.9071 | |
|
|
| cosine_precision@1 | 0.7171 | |
|
|
| cosine_precision@3 | 0.2805 | |
|
|
| cosine_precision@5 | 0.174 | |
|
|
| cosine_precision@10 | 0.0907 | |
|
|
| cosine_recall@1 | 0.7171 | |
|
|
| cosine_recall@3 | 0.8414 | |
|
|
| cosine_recall@5 | 0.87 | |
|
|
| cosine_recall@10 | 0.9071 | |
|
|
| **cosine_ndcg@10** | **0.8148** | |
|
|
| cosine_mrr@10 | 0.785 | |
|
|
| cosine_map@100 | 0.7888 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_512` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 512 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7171 | |
|
|
| cosine_accuracy@3 | 0.8471 | |
|
|
| cosine_accuracy@5 | 0.8671 | |
|
|
| cosine_accuracy@10 | 0.9086 | |
|
|
| cosine_precision@1 | 0.7171 | |
|
|
| cosine_precision@3 | 0.2824 | |
|
|
| cosine_precision@5 | 0.1734 | |
|
|
| cosine_precision@10 | 0.0909 | |
|
|
| cosine_recall@1 | 0.7171 | |
|
|
| cosine_recall@3 | 0.8471 | |
|
|
| cosine_recall@5 | 0.8671 | |
|
|
| cosine_recall@10 | 0.9086 | |
|
|
| **cosine_ndcg@10** | **0.8147** | |
|
|
| cosine_mrr@10 | 0.7844 | |
|
|
| cosine_map@100 | 0.788 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_256` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 256 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7171 | |
|
|
| cosine_accuracy@3 | 0.8414 | |
|
|
| cosine_accuracy@5 | 0.8729 | |
|
|
| cosine_accuracy@10 | 0.9014 | |
|
|
| cosine_precision@1 | 0.7171 | |
|
|
| cosine_precision@3 | 0.2805 | |
|
|
| cosine_precision@5 | 0.1746 | |
|
|
| cosine_precision@10 | 0.0901 | |
|
|
| cosine_recall@1 | 0.7171 | |
|
|
| cosine_recall@3 | 0.8414 | |
|
|
| cosine_recall@5 | 0.8729 | |
|
|
| cosine_recall@10 | 0.9014 | |
|
|
| **cosine_ndcg@10** | **0.8139** | |
|
|
| cosine_mrr@10 | 0.7854 | |
|
|
| cosine_map@100 | 0.7895 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_128` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 128 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.7129 | |
|
|
| cosine_accuracy@3 | 0.8243 | |
|
|
| cosine_accuracy@5 | 0.8586 | |
|
|
| cosine_accuracy@10 | 0.9029 | |
|
|
| cosine_precision@1 | 0.7129 | |
|
|
| cosine_precision@3 | 0.2748 | |
|
|
| cosine_precision@5 | 0.1717 | |
|
|
| cosine_precision@10 | 0.0903 | |
|
|
| cosine_recall@1 | 0.7129 | |
|
|
| cosine_recall@3 | 0.8243 | |
|
|
| cosine_recall@5 | 0.8586 | |
|
|
| cosine_recall@10 | 0.9029 | |
|
|
| **cosine_ndcg@10** | **0.8072** | |
|
|
| cosine_mrr@10 | 0.7766 | |
|
|
| cosine_map@100 | 0.7803 | |
|
|
|
|
|
#### Information Retrieval |
|
|
|
|
|
* Dataset: `dim_64` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"truncate_dim": 64 |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.6914 | |
|
|
| cosine_accuracy@3 | 0.7943 | |
|
|
| cosine_accuracy@5 | 0.8314 | |
|
|
| cosine_accuracy@10 | 0.8729 | |
|
|
| cosine_precision@1 | 0.6914 | |
|
|
| cosine_precision@3 | 0.2648 | |
|
|
| cosine_precision@5 | 0.1663 | |
|
|
| cosine_precision@10 | 0.0873 | |
|
|
| cosine_recall@1 | 0.6914 | |
|
|
| cosine_recall@3 | 0.7943 | |
|
|
| cosine_recall@5 | 0.8314 | |
|
|
| cosine_recall@10 | 0.8729 | |
|
|
| **cosine_ndcg@10** | **0.7805** | |
|
|
| cosine_mrr@10 | 0.7512 | |
|
|
| cosine_map@100 | 0.7554 | |
|
|
|
|
|
<!-- |
|
|
## 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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--> |
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<!-- |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Dataset |
|
|
|
|
|
#### json |
|
|
|
|
|
* Dataset: json |
|
|
* Size: 6,300 training samples |
|
|
* Columns: <code>anchor</code> and <code>positive</code> |
|
|
* Approximate statistics based on the first 1000 samples: |
|
|
| | anchor | positive | |
|
|
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
|
| type | string | string | |
|
|
| details | <ul><li>min: 7 tokens</li><li>mean: 20.53 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 44.95 tokens</li><li>max: 512 tokens</li></ul> | |
|
|
* Samples: |
|
|
| anchor | positive | |
|
|
|:----------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| <code>What does the No Surprises Act require providers to develop and disclose?</code> | <code>Under the No Surprises Act, which went into effect January 1, 2022, certain providers, including DaVita, are required to develop and disclose a 'Good Faith Estimate' that details the expected charges for furnishing certain items or services.</code> | |
|
|
| <code>What does Gross Merchandise Volume (GMV) represent in financial terms?</code> | <code>GMV consists of the total value of all paid transactions between users on our platforms during the applicable period inclusive of shipping fees and taxes.</code> | |
|
|
| <code>What was the pre-tax restructuring charge for the fiscal year 2023 related to the discontinuation of certain R&D programs?</code> | <code>The pre-tax restructuring charge of approximately $0.5 billion in the fiscal year 2023 included the termination of partnered and non-partnered program costs and asset impairments.</code> | |
|
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"loss": "MultipleNegativesRankingLoss", |
|
|
"matryoshka_dims": [ |
|
|
768, |
|
|
512, |
|
|
256, |
|
|
128, |
|
|
64 |
|
|
], |
|
|
"matryoshka_weights": [ |
|
|
1, |
|
|
1, |
|
|
1, |
|
|
1, |
|
|
1 |
|
|
], |
|
|
"n_dims_per_step": -1 |
|
|
} |
|
|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `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 |
|
|
- `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 |
|
|
- `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 |
|
|
- `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, '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`: False |
|
|
- `hub_always_push`: False |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `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 |
|
|
- `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 | |
|
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
|
| 0.9697 | 6 | - | 0.7993 | 0.7967 | 0.7941 | 0.7845 | 0.7431 | |
|
|
| 1.6162 | 10 | 2.3395 | - | - | - | - | - | |
|
|
| 1.9394 | 12 | - | 0.8089 | 0.8086 | 0.8108 | 0.8007 | 0.7669 | |
|
|
| 2.9091 | 18 | - | 0.8158 | 0.8134 | 0.8144 | 0.8066 | 0.7761 | |
|
|
| 3.2323 | 20 | 1.0419 | - | - | - | - | - | |
|
|
| **3.8788** | **24** | **-** | **0.8148** | **0.8147** | **0.8139** | **0.8072** | **0.7805** | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.12.6 |
|
|
- Sentence Transformers: 4.1.0 |
|
|
- Transformers: 4.40.2 |
|
|
- PyTorch: 2.6.0+cu124 |
|
|
- Accelerate: 1.6.0 |
|
|
- Datasets: 2.19.1 |
|
|
- Tokenizers: 0.19.1 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### 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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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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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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