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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: Americas | $ | 7,631,647 | | | $ | 6,817,454 | | 79.3 | % | 84.1 |
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| % |
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sentences: |
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- What therapeutic area does the folate receptor alpha antibody drug conjugate MBK-103 |
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target? |
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- What was the proportion of Americas' net revenue to the company's total net revenue |
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in 2023, and how did it change from 2022? |
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- What was the Company's income tax provision for the year ended December 31, 2022? |
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- source_sentence: The Company establishes SSP based on observable prices of products |
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or services sold or priced separately in comparable circumstances to similar customers. |
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sentences: |
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- What were the lease terms and discount rates for operating leases as of March |
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31, 2023 and 2022? |
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- What factors influence the Company's ability to establish Standalone Selling Prices |
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(SSP) based on observable prices? |
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- What number is associated with Item 8 in the document? |
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- source_sentence: Our effective tax rates could be affected by numerous factors, |
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such as changes in our business operations, acquisitions, investments, entry into |
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new businesses and geographies, intercompany transactions, the relative amount |
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of our foreign earnings, including earnings being lower than anticipated in jurisdictions |
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where we have lower statutory rates and higher than anticipated in jurisdictions |
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where we have higher statutory rates, losses incurred in jurisdictions for which |
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we are not able to realize related tax benefits, the applicability of special |
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tax regimes, changes in foreign exchange rates, changes in our stock price, changes |
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to our forecasts of income and loss and the mix of jurisdictions to which they |
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relate, changes in our deferred tax assets and liabilities and their valuation, |
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changes in the laws, regulations, administrative practices, principles, and interpretations |
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related to tax, including changes to the global tax framework, competition, and |
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other laws and accounting rules in various jurisdictions. |
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sentences: |
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- What impact do tax laws and economic conditions have on the company's effective |
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tax rates? |
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- What is the purpose of Alphabet Inc.'s annual review of methodologies used in |
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monitoring advertising metrics? |
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- From which sources does Apple obtain certain essential components? |
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- source_sentence: (Decrease) increase in cash, cash equivalents and restricted cash |
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for fiscal year 2023 was a decrease of $182 million, starting with $4,763 million |
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at the beginning and ending with $4,581 million. |
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sentences: |
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- What is the minimum project cost for the development described in the Second Development |
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Agreement involving MBS? |
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- What does the No Surprises Act require providers to develop and disclose? |
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- What was the change in cash and cash equivalents for Hewlett Packard Enterprise |
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from the beginning to the end of the fiscal year 2023? |
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- source_sentence: The total amount of gross unrecognized tax benefits as of December |
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30, 2023 was $13,571. |
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sentences: |
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- What was the total amount of gross unrecognized tax benefits as of December 30, |
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2023? |
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- What percentage of Kenvue Common Stock did Johnson & Johnson own as of the closing |
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of the IPO? |
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- What was the percentage change in sales from 2022 to 2023 for the Trauma segment |
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in the U.S.? |
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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: 0.6928571428571428 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8142857142857143 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8471428571428572 |
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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: 0.6928571428571428 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2714285714285714 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1694285714285714 |
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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: 0.6928571428571428 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8142857142857143 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8471428571428572 |
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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: 0.7960400928582716 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7625391156462585 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7656459931357954 |
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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: 0.7 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8142857142857143 |
|
name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.85 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8928571428571429 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2714285714285714 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.16999999999999998 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.08928571428571426 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8142857142857143 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.85 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8928571428571429 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7962092633155669 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7652437641723353 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7690571344301111 |
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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: 0.6885714285714286 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8085714285714286 |
|
name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
|
value: 0.8485714285714285 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8928571428571429 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6885714285714286 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2695238095238095 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16971428571428568 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08928571428571427 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6885714285714286 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8085714285714286 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8485714285714285 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8928571428571429 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.790294082455236 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7575634920634915 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7608461966590305 |
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name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
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name: Information Retrieval |
|
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: 0.6771428571428572 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7971428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.83 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.89 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6771428571428572 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26571428571428574 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16599999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.089 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6771428571428572 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7971428571428572 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.83 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.89 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7811390356263523 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7466921768707482 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7500930927741866 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6457142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7685714285714286 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8114285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8628571428571429 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6457142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2561904761904762 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16228571428571428 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08628571428571427 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6457142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7685714285714286 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8114285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8628571428571429 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7526448867884948 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7175549886621314 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.721601645358737 |
|
name: Cosine Map@100 |
|
--- |
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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) 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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|
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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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|
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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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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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|
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First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
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|
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Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
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|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("michalwilkosz/bge-base-financial-matryoshka") |
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# Run inference |
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sentences = [ |
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'The total amount of gross unrecognized tax benefits as of December 30, 2023 was $13,571.', |
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'What was the total amount of gross unrecognized tax benefits as of December 30, 2023?', |
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'What percentage of Kenvue Common Stock did Johnson & Johnson own as of the closing of the IPO?', |
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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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|
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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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|
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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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|
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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 | 0.6929 | 0.7 | 0.6886 | 0.6771 | 0.6457 | |
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| cosine_accuracy@3 | 0.8143 | 0.8143 | 0.8086 | 0.7971 | 0.7686 | |
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| cosine_accuracy@5 | 0.8471 | 0.85 | 0.8486 | 0.83 | 0.8114 | |
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| cosine_accuracy@10 | 0.9014 | 0.8929 | 0.8929 | 0.89 | 0.8629 | |
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| cosine_precision@1 | 0.6929 | 0.7 | 0.6886 | 0.6771 | 0.6457 | |
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| cosine_precision@3 | 0.2714 | 0.2714 | 0.2695 | 0.2657 | 0.2562 | |
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| cosine_precision@5 | 0.1694 | 0.17 | 0.1697 | 0.166 | 0.1623 | |
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| cosine_precision@10 | 0.0901 | 0.0893 | 0.0893 | 0.089 | 0.0863 | |
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| cosine_recall@1 | 0.6929 | 0.7 | 0.6886 | 0.6771 | 0.6457 | |
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| cosine_recall@3 | 0.8143 | 0.8143 | 0.8086 | 0.7971 | 0.7686 | |
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| cosine_recall@5 | 0.8471 | 0.85 | 0.8486 | 0.83 | 0.8114 | |
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| cosine_recall@10 | 0.9014 | 0.8929 | 0.8929 | 0.89 | 0.8629 | |
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| **cosine_ndcg@10** | **0.796** | **0.7962** | **0.7903** | **0.7811** | **0.7526** | |
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| cosine_mrr@10 | 0.7625 | 0.7652 | 0.7576 | 0.7467 | 0.7176 | |
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| cosine_map@100 | 0.7656 | 0.7691 | 0.7608 | 0.7501 | 0.7216 | |
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|
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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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--> |
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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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|
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### Training Dataset |
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|
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#### json |
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|
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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: 2 tokens</li><li>mean: 45.43 tokens</li><li>max: 301 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.34 tokens</li><li>max: 46 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| |
|
| <code>Almost all FedEx Office locations provide local pickup-and-delivery service for print jobs completed by FedEx Office. A FedEx courier picks up a customer’s print job at the customer’s location and then returns the finished product to the customer.</code> | <code>What service does almost all FedEx Office locations provide for completed print jobs?</code> | |
|
| <code>Non-compliance with government laws and regulations may result in fines, limits on the ability to sell products, suspension of business activities, reputational damage, and legal liabilities.</code> | <code>What are the consequences of failing to comply with government laws and regulations?</code> | |
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| <code>Item 8 is labeled as Financial Statements and Supplementary Data.</code> | <code>What is the title of Item 8 in the financial document?</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 |
|
], |
|
"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 |
|
- `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`: 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 |
|
- `batch_eval_metrics`: 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 | 1.5678 | - | - | - | - | - | |
|
| 0.9746 | 12 | - | 0.7840 | 0.7835 | 0.7763 | 0.7656 | 0.7360 | |
|
| 1.6244 | 20 | 0.6336 | - | - | - | - | - | |
|
| 1.9492 | 24 | - | 0.7960 | 0.7950 | 0.7903 | 0.7783 | 0.7500 | |
|
| 2.4365 | 30 | 0.464 | - | - | - | - | - | |
|
| **2.9239** | **36** | **-** | **0.7965** | **0.7969** | **0.7912** | **0.7825** | **0.7525** | |
|
| 3.2487 | 40 | 0.3768 | - | - | - | - | - | |
|
| 3.8985 | 48 | - | 0.7960 | 0.7962 | 0.7903 | 0.7811 | 0.7526 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.11.11 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 1.2.0 |
|
- 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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