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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: Goodwill is recognized for the excess of the purchase price over |
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the fair value of tangible and identifiable intangible net assets of businesses |
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acquired. In evaluating goodwill impairment, a qualitative assessment is performed |
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to determine the likelihood that the fair value of a reporting unit is less than |
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its carrying amount. This might lead to further testing of goodwill for impairment, |
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which includes comparing the fair value of the reporting unit to its carrying |
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value (including attributable goodwill). Fair value for our reporting units is |
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determined using an income or market approach incorporating market participant |
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considerations and management’s assumptions. |
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sentences: |
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- How is goodwill reviewed for impairment in a company, and what methods are used |
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to determine the fair value of reporting units? |
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- What regulatory framework does the FCC currently apply to broadband internet access |
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services as of 2023? |
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- What were the total interest payments made by the company in 2023, 2022, and 2021? |
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- source_sentence: Part IV Item 15, titled 'Exhibits, Financial Statement Schedules', |
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includes the 'Index to Financial Statements' and the 'Index to Financial Statement |
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Schedules.' |
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sentences: |
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- What was the net cash from operations reported for the year ended June 30, 2023? |
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- Where would one find the 'Index to Financial Statements' and the 'Index to Financial |
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Statement Schedules' mentioned? |
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- What is the trajectory of the AMPTC for microinverters starting in 2030? |
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- source_sentence: As of December 31, 2023, the total amortized cost, net of valuation |
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allowance, for non-U.S. government securities amounted to $14,516 million. |
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sentences: |
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- How much federal net operating loss carryforwards did the company have at the |
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end of 2023, and how much of it is expected to be realized? |
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- What accounting principles are followed in the preparation of Goldman Sachs' consolidated |
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financial statements for 2023? |
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- What was the total amortized cost, net of valuation allowance, for non-U.S. government |
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securities as of December 31, 2023? |
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- source_sentence: Information about legal proceedings in the Annual Report on Form |
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10-K is incorporated by reference under several notes and sections. |
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sentences: |
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- What method is used to provide information about legal proceedings in the Annual |
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Report on Form 10-K? |
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- What can cause delays in pharmaceutical product launches? |
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- What was the total amount of cash dividends declared by the company per share |
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in the fiscal year ending on October 1, 2023? |
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- source_sentence: MERS database revenues contain multiple performance obligations |
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related to each new loan registration and future transfers, and the revenues are |
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primarily recorded at the point in time of each transaction. |
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sentences: |
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- How are revenues from MERS database recognized? |
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- How did The Home Depot, Inc.'s basic earnings per share change from 2020 to 2022? |
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- How many active sellers and buyers did Etsy's marketplaces connect as of December |
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31, 2023? |
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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: 0.7228571428571429 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8528571428571429 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.89 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9185714285714286 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7228571428571429 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2842857142857143 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17799999999999996 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09185714285714283 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7228571428571429 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8528571428571429 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.89 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9185714285714286 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8244010006831627 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7936836734693877 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7971656786986449 |
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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: 0.7157142857142857 |
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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: 0.8857142857142857 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9185714285714286 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7157142857142857 |
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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: 0.17714285714285713 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09185714285714283 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7157142857142857 |
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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: 0.8857142857142857 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9185714285714286 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8209116379330612 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7891343537414967 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7926472335071902 |
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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: 0.71 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8428571428571429 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8757142857142857 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9128571428571428 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
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value: 0.71 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28095238095238095 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17514285714285713 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09128571428571428 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
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value: 0.71 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8428571428571429 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8757142857142857 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9128571428571428 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8138965576076403 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7818429705215417 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7855894139852542 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.6871428571428572 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8185714285714286 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8628571428571429 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8971428571428571 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6871428571428572 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.27285714285714285 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17257142857142854 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0897142857142857 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6871428571428572 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8185714285714286 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8628571428571429 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8971428571428571 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7953389524625682 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7622392290249432 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7667451557566504 |
|
name: Cosine Map@100 |
|
- task: |
|
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 |
|
value: 0.6628571428571428 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7842857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8371428571428572 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8771428571428571 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6628571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26142857142857145 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1674285714285714 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0877142857142857 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6628571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7842857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8371428571428572 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8771428571428571 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7701231991584621 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7357777777777779 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7410692697767751 |
|
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 |
|
- **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 |
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|
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### Model Sources |
|
|
|
- **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) |
|
|
|
### 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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First install the Sentence Transformers library: |
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|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
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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 |
|
model = SentenceTransformer("chcho/bge-base-financial-matryoshka") |
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# Run inference |
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sentences = [ |
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'MERS database revenues contain multiple performance obligations related to each new loan registration and future transfers, and the revenues are primarily recorded at the point in time of each transaction.', |
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'How are revenues from MERS database recognized?', |
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"How many active sellers and buyers did Etsy's marketplaces connect as of December 31, 2023?", |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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|
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<!-- |
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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|
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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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You can finetune this model on your own dataset. |
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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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|
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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 | 0.7229 | 0.7157 | 0.71 | 0.6871 | 0.6629 | |
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| cosine_accuracy@3 | 0.8529 | 0.8471 | 0.8429 | 0.8186 | 0.7843 | |
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| cosine_accuracy@5 | 0.89 | 0.8857 | 0.8757 | 0.8629 | 0.8371 | |
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| cosine_accuracy@10 | 0.9186 | 0.9186 | 0.9129 | 0.8971 | 0.8771 | |
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| cosine_precision@1 | 0.7229 | 0.7157 | 0.71 | 0.6871 | 0.6629 | |
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| cosine_precision@3 | 0.2843 | 0.2824 | 0.281 | 0.2729 | 0.2614 | |
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| cosine_precision@5 | 0.178 | 0.1771 | 0.1751 | 0.1726 | 0.1674 | |
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| cosine_precision@10 | 0.0919 | 0.0919 | 0.0913 | 0.0897 | 0.0877 | |
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| cosine_recall@1 | 0.7229 | 0.7157 | 0.71 | 0.6871 | 0.6629 | |
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| cosine_recall@3 | 0.8529 | 0.8471 | 0.8429 | 0.8186 | 0.7843 | |
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| cosine_recall@5 | 0.89 | 0.8857 | 0.8757 | 0.8629 | 0.8371 | |
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| cosine_recall@10 | 0.9186 | 0.9186 | 0.9129 | 0.8971 | 0.8771 | |
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| **cosine_ndcg@10** | **0.8244** | **0.8209** | **0.8139** | **0.7953** | **0.7701** | |
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| cosine_mrr@10 | 0.7937 | 0.7891 | 0.7818 | 0.7622 | 0.7358 | |
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| cosine_map@100 | 0.7972 | 0.7926 | 0.7856 | 0.7667 | 0.7411 | |
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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: |
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| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 2 tokens</li><li>mean: 46.48 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.5 tokens</li><li>max: 51 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| |
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| <code>We use a variety of methodologies to determine the fair value of these assets, including discounted cash flow models, which include assumptions we believe are consistent with those a market participant would use.</code> | <code>How is the fair value of intangible assets determined within a company?</code> | |
|
| <code>We continue to own a 35% minority ownership in Gentiva Hospice operations after it was restructured into a new stand-alone company.</code> | <code>What percentage minority ownership does the company retain in Gentiva Hospice after the restructuring?</code> | |
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| <code>The net interest income for the first quarter of 2023 was $14,448 million.</code> | <code>What was the net interest income for the first quarter of 2023?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
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512, |
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256, |
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128, |
|
64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
|
1, |
|
1, |
|
1 |
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], |
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"n_dims_per_step": -1 |
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} |
|
``` |
|
|
|
### 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 | 1.5791 | - | - | - | - | - | |
|
| 0.9746 | 12 | - | 0.8089 | 0.8028 | 0.7958 | 0.7714 | 0.7428 | |
|
| 1.6244 | 20 | 0.6637 | - | - | - | - | - | |
|
| 1.9492 | 24 | - | 0.8209 | 0.8166 | 0.8109 | 0.7913 | 0.7615 | |
|
| 2.4365 | 30 | 0.5072 | - | - | - | - | - | |
|
| **2.9239** | **36** | **-** | **0.8229** | **0.82** | **0.8133** | **0.7959** | **0.7704** | |
|
| 3.2487 | 40 | 0.394 | - | - | - | - | - | |
|
| 3.8985 | 48 | - | 0.8244 | 0.8209 | 0.8139 | 0.7953 | 0.7701 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.9.5 |
|
- Sentence Transformers: 3.3.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.27.2 |
|
- 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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