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---
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language:
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- en
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license: apache-2.0
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library_name: sentence-transformers
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- 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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datasets: []
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metrics:
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- cosine_accuracy@1
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- cosine_accuracy@3
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- cosine_accuracy@5
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- cosine_accuracy@10
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- cosine_precision@1
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- cosine_precision@3
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- cosine_precision@5
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- cosine_precision@10
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- cosine_recall@1
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- cosine_recall@3
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- cosine_recall@5
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- cosine_recall@10
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- cosine_ndcg@10
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- cosine_mrr@10
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- cosine_map@100
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widget:
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- source_sentence: Item 3—Legal Proceedings See discussion of Legal Proceedings in
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Note 10 to the consolidated financial statements included in Item 8 of this Report.
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sentences:
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- How much did the company's finance lease obligations total as of December 31,
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2023?
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- What do Note 10 and Item 8 of the report encompass?
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- What was the basic earnings per common share attributable to Comcast Corporation
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shareholders in 2023?
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- source_sentence: Our quarterly Insurance segment earnings and operating cash flows
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are impacted by the Medicare Part D benefit Grant program, the changing membership
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composition, and the multistage plan period starting annually on January 1. These
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plan designs generally result in us sharing a greater portion of the responsibility
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for total prescription drug costs in the early stages and less in the latter stages.
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sentences:
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- What are the two main categories into which Ford Motor Company classifies its
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costs and expenses, excluding those related to Ford Credit?
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- How does the benefit design of Medicare Part D impact the quarterly insurance
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segment earnings and operating cash flows?
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- What basis is used to record HTM investment securities in Schwab's financial statements?
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- source_sentence: Operating Profit in the Wizards of the Coast and Digital Gaming
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segment decreased 2% to $538.3 million.
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sentences:
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- How much did the Wizards of the Coast and Digital Gaming segment's operating profit
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change in 2022?
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- What factors are considered in evaluating the lifetime losses for most loans and
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receivables?
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- How did the loss on certain U.S. affiliates impact the Company's effective tax
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rate in the fiscal fourth quarter of 2021?
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- source_sentence: In 2023, the net earnings of Johnson & Johnson were $35,153 million.
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The company also registered cash dividends paid amounting to $11,770 million for
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the year, priced at $4.70 per share.
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sentences:
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- What was the postpaid churn rate for AT&T Inc. in 2023?
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- What was the GAAP net revenue for the fiscal year ended October 31, 2023?
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- What were the total net earnings of Johnson & Johnson in the year 2023?
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- source_sentence: During fiscal 2022, GameStop Corp increased its valuation allowances
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by approximately $70.2 million in various jurisdictions.
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sentences:
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- How much did GameStop Corp's valuation allowances increase during fiscal 2022?
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- How does Gilead ensure an inclusive and diverse workforce?
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- What factors are considered in determining the estimated future warranty costs
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for connected fitness and Precor branded fitness products?
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pipeline_tag: sentence-similarity
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model-index:
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- name: BGE base Financial Matryoshka
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results:
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- task:
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type: information-retrieval
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name: Information Retrieval
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dataset:
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name: dim 768
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type: dim_768
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metrics:
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- type: cosine_accuracy@1
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value: 0.7185714285714285
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name: Cosine Accuracy@1
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- type: cosine_accuracy@3
|
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value: 0.83
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name: Cosine Accuracy@3
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- type: cosine_accuracy@5
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value: 0.8714285714285714
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name: Cosine Accuracy@5
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- type: cosine_accuracy@10
|
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value: 0.91
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name: Cosine Accuracy@10
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- type: cosine_precision@1
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value: 0.7185714285714285
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name: Cosine Precision@1
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- type: cosine_precision@3
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value: 0.27666666666666667
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name: Cosine Precision@3
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- type: cosine_precision@5
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value: 0.17428571428571427
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name: Cosine Precision@5
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- type: cosine_precision@10
|
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value: 0.091
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name: Cosine Precision@10
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- type: cosine_recall@1
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value: 0.7185714285714285
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name: Cosine Recall@1
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- type: cosine_recall@3
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value: 0.83
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name: Cosine Recall@3
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- type: cosine_recall@5
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value: 0.8714285714285714
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name: Cosine Recall@5
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- type: cosine_recall@10
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value: 0.91
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name: Cosine Recall@10
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- type: cosine_ndcg@10
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value: 0.8137967516958747
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name: Cosine Ndcg@10
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- type: cosine_mrr@10
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value: 0.7830442176870747
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name: Cosine Mrr@10
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- type: cosine_map@100
|
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value: 0.7866777593387027
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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: 0.7114285714285714
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name: Cosine Accuracy@1
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- type: cosine_accuracy@3
|
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value: 0.8314285714285714
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name: Cosine Accuracy@3
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- type: cosine_accuracy@5
|
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value: 0.8728571428571429
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name: Cosine Accuracy@5
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- type: cosine_accuracy@10
|
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value: 0.9142857142857143
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name: Cosine Accuracy@10
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- type: cosine_precision@1
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value: 0.7114285714285714
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name: Cosine Precision@1
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- type: cosine_precision@3
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value: 0.27714285714285714
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name: Cosine Precision@3
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- type: cosine_precision@5
|
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value: 0.17457142857142854
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name: Cosine Precision@5
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- type: cosine_precision@10
|
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value: 0.09142857142857141
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name: Cosine Precision@10
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- type: cosine_recall@1
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value: 0.7114285714285714
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name: Cosine Recall@1
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- type: cosine_recall@3
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value: 0.8314285714285714
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name: Cosine Recall@3
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- type: cosine_recall@5
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value: 0.8728571428571429
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name: Cosine Recall@5
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- type: cosine_recall@10
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value: 0.9142857142857143
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name: Cosine Recall@10
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- type: cosine_ndcg@10
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value: 0.8123538841130576
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name: Cosine Ndcg@10
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- type: cosine_mrr@10
|
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value: 0.7798667800453513
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name: Cosine Mrr@10
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- type: cosine_map@100
|
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value: 0.7831580648041446
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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: 0.7
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name: Cosine Accuracy@1
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- type: cosine_accuracy@3
|
|
value: 0.8285714285714286
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.8614285714285714
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.9042857142857142
|
|
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: 0.2761904761904762
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.17228571428571426
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
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value: 0.09042857142857143
|
|
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: 0.8285714285714286
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name: Cosine Recall@3
|
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- type: cosine_recall@5
|
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value: 0.8614285714285714
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
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value: 0.9042857142857142
|
|
name: Cosine Recall@10
|
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- type: cosine_ndcg@10
|
|
value: 0.8043112987059042
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|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.7721706349206346
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|
name: Cosine Mrr@10
|
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- type: cosine_map@100
|
|
value: 0.7759026470022171
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name: Cosine Map@100
|
|
- task:
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type: information-retrieval
|
|
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: 0.6857142857142857
|
|
name: Cosine Accuracy@1
|
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- type: cosine_accuracy@3
|
|
value: 0.8071428571428572
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.8571428571428571
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.8971428571428571
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.6857142857142857
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.26904761904761904
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.1714285714285714
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.0897142857142857
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.6857142857142857
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.8071428571428572
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.8571428571428571
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.8971428571428571
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.79087795854059
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.7568854875283447
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.7608935817550728
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: dim 64
|
|
type: dim_64
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.66
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.7757142857142857
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.8128571428571428
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.8671428571428571
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.66
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.25857142857142856
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.16257142857142853
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.0867142857142857
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.66
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.7757142857142857
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.8128571428571428
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.8671428571428571
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.7616045249840884
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.7281247165532877
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.7330922421864847
|
|
name: Cosine Map@100
|
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---
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|
|
# BGE base Financial Matryoshka
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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|
|
## Model Details
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|
|
### Model Description
|
|
- **Model Type:** Sentence Transformer
|
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
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- **Maximum Sequence Length:** 512 tokens
|
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- **Output Dimensionality:** 768 tokens
|
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- **Similarity Function:** Cosine Similarity
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<!-- - **Training Dataset:** Unknown -->
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- **Language:** en
|
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- **License:** apache-2.0
|
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|
|
### Model Sources
|
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|
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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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|
|
## Usage
|
|
|
|
### Direct Usage (Sentence Transformers)
|
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|
|
First install the Sentence Transformers library:
|
|
|
|
```bash
|
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pip install -U sentence-transformers
|
|
```
|
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|
|
Then you can load this model and run inference.
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
# Download from the 🤗 Hub
|
|
model = SentenceTransformer("cristuf/bge-base-financial-matryoshka")
|
|
# Run inference
|
|
sentences = [
|
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'During fiscal 2022, GameStop Corp increased its valuation allowances by approximately $70.2 million in various jurisdictions.',
|
|
"How much did GameStop Corp's valuation allowances increase during fiscal 2022?",
|
|
'How does Gilead ensure an inclusive and diverse workforce?',
|
|
]
|
|
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]
|
|
```
|
|
|
|
<!--
|
|
### Direct Usage (Transformers)
|
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary>
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|
|
</details>
|
|
-->
|
|
|
|
<!--
|
|
### Downstream Usage (Sentence Transformers)
|
|
|
|
You can finetune this model on your own dataset.
|
|
|
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<details><summary>Click to expand</summary>
|
|
|
|
</details>
|
|
-->
|
|
|
|
<!--
|
|
### Out-of-Scope Use
|
|
|
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
|
-->
|
|
|
|
## Evaluation
|
|
|
|
### Metrics
|
|
|
|
#### Information Retrieval
|
|
* Dataset: `dim_768`
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
|
|
|
| Metric | Value |
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|
|:--------------------|:-----------|
|
|
| cosine_accuracy@1 | 0.7186 |
|
|
| cosine_accuracy@3 | 0.83 |
|
|
| cosine_accuracy@5 | 0.8714 |
|
|
| cosine_accuracy@10 | 0.91 |
|
|
| cosine_precision@1 | 0.7186 |
|
|
| cosine_precision@3 | 0.2767 |
|
|
| cosine_precision@5 | 0.1743 |
|
|
| cosine_precision@10 | 0.091 |
|
|
| cosine_recall@1 | 0.7186 |
|
|
| cosine_recall@3 | 0.83 |
|
|
| cosine_recall@5 | 0.8714 |
|
|
| cosine_recall@10 | 0.91 |
|
|
| cosine_ndcg@10 | 0.8138 |
|
|
| cosine_mrr@10 | 0.783 |
|
|
| **cosine_map@100** | **0.7867** |
|
|
|
|
#### Information Retrieval
|
|
* Dataset: `dim_512`
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
|
|
|
| Metric | Value |
|
|
|:--------------------|:-----------|
|
|
| cosine_accuracy@1 | 0.7114 |
|
|
| cosine_accuracy@3 | 0.8314 |
|
|
| cosine_accuracy@5 | 0.8729 |
|
|
| cosine_accuracy@10 | 0.9143 |
|
|
| cosine_precision@1 | 0.7114 |
|
|
| cosine_precision@3 | 0.2771 |
|
|
| cosine_precision@5 | 0.1746 |
|
|
| cosine_precision@10 | 0.0914 |
|
|
| cosine_recall@1 | 0.7114 |
|
|
| cosine_recall@3 | 0.8314 |
|
|
| cosine_recall@5 | 0.8729 |
|
|
| cosine_recall@10 | 0.9143 |
|
|
| cosine_ndcg@10 | 0.8124 |
|
|
| cosine_mrr@10 | 0.7799 |
|
|
| **cosine_map@100** | **0.7832** |
|
|
|
|
#### Information Retrieval
|
|
* Dataset: `dim_256`
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
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|
| Metric | Value |
|
|
|:--------------------|:-----------|
|
|
| cosine_accuracy@1 | 0.7 |
|
|
| cosine_accuracy@3 | 0.8286 |
|
|
| cosine_accuracy@5 | 0.8614 |
|
|
| cosine_accuracy@10 | 0.9043 |
|
|
| cosine_precision@1 | 0.7 |
|
|
| cosine_precision@3 | 0.2762 |
|
|
| cosine_precision@5 | 0.1723 |
|
|
| cosine_precision@10 | 0.0904 |
|
|
| cosine_recall@1 | 0.7 |
|
|
| cosine_recall@3 | 0.8286 |
|
|
| cosine_recall@5 | 0.8614 |
|
|
| cosine_recall@10 | 0.9043 |
|
|
| cosine_ndcg@10 | 0.8043 |
|
|
| cosine_mrr@10 | 0.7722 |
|
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| **cosine_map@100** | **0.7759** |
|
|
|
|
#### Information Retrieval
|
|
* Dataset: `dim_128`
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
|
|
|
| Metric | Value |
|
|
|:--------------------|:-----------|
|
|
| cosine_accuracy@1 | 0.6857 |
|
|
| cosine_accuracy@3 | 0.8071 |
|
|
| cosine_accuracy@5 | 0.8571 |
|
|
| cosine_accuracy@10 | 0.8971 |
|
|
| cosine_precision@1 | 0.6857 |
|
|
| cosine_precision@3 | 0.269 |
|
|
| cosine_precision@5 | 0.1714 |
|
|
| cosine_precision@10 | 0.0897 |
|
|
| cosine_recall@1 | 0.6857 |
|
|
| cosine_recall@3 | 0.8071 |
|
|
| cosine_recall@5 | 0.8571 |
|
|
| cosine_recall@10 | 0.8971 |
|
|
| cosine_ndcg@10 | 0.7909 |
|
|
| cosine_mrr@10 | 0.7569 |
|
|
| **cosine_map@100** | **0.7609** |
|
|
|
|
#### Information Retrieval
|
|
* Dataset: `dim_64`
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
|
|
|
| Metric | Value |
|
|
|:--------------------|:-----------|
|
|
| cosine_accuracy@1 | 0.66 |
|
|
| cosine_accuracy@3 | 0.7757 |
|
|
| cosine_accuracy@5 | 0.8129 |
|
|
| cosine_accuracy@10 | 0.8671 |
|
|
| cosine_precision@1 | 0.66 |
|
|
| cosine_precision@3 | 0.2586 |
|
|
| cosine_precision@5 | 0.1626 |
|
|
| cosine_precision@10 | 0.0867 |
|
|
| cosine_recall@1 | 0.66 |
|
|
| cosine_recall@3 | 0.7757 |
|
|
| cosine_recall@5 | 0.8129 |
|
|
| cosine_recall@10 | 0.8671 |
|
|
| cosine_ndcg@10 | 0.7616 |
|
|
| cosine_mrr@10 | 0.7281 |
|
|
| **cosine_map@100** | **0.7331** |
|
|
|
|
<!--
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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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-->
|
|
|
|
## Training Details
|
|
|
|
### Training Dataset
|
|
|
|
#### Unnamed Dataset
|
|
|
|
|
|
* Size: 6,300 training samples
|
|
* Columns: <code>positive</code> and <code>anchor</code>
|
|
* Approximate statistics based on the first 1000 samples:
|
|
| | positive | anchor |
|
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
|
|
| type | string | string |
|
|
| details | <ul><li>min: 8 tokens</li><li>mean: 46.36 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.41 tokens</li><li>max: 51 tokens</li></ul> |
|
|
* Samples:
|
|
| positive | anchor |
|
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|
|
|
| <code>Japan's revenue for the year 2023 reached 2,367.0 million.</code> | <code>What was the revenue attributed to Japan in the year 2023?</code> |
|
|
| <code>Our four reportable segments are: •the Data Center segment, which primarily includes server CPUs, GPUs, APUs, DPUs, FPGAs, SmartNICs, AI accelerators and Adaptive SoC products for data centers; •the Client segment, which primarily includes CPUs, APUs, and chipsets for desktop, notebook and handheld personal computers; •the Gaming segment, which primarily includes discrete GPUs, semi-custom SoC products and development services; and •the Embedded segment, which primarily includes embedded CPUs, GPUs, APUs, FPGAs, SOMs, and Adaptive SoC products.</code> | <code>What are the different segments that AMD reports financially?</code> |
|
|
| <code>For detailed information about the company's legal proceedings, see Note 4 to the consolidated financial statements, included under the caption 'Contingencies' in the Annual Report on Form 10-K.</code> | <code>Where can detailed information about the company's legal proceedings be found in its financial statements?</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",
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|
"matryoshka_dims": [
|
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768,
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512,
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256,
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128,
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64
|
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],
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"matryoshka_weights": [
|
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1,
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1,
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1,
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1,
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1
|
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],
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"n_dims_per_step": -1
|
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}
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|
```
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### Training Hyperparameters
|
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#### Non-Default Hyperparameters
|
|
|
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- `eval_strategy`: epoch
|
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- `per_device_train_batch_size`: 32
|
|
- `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
|
|
- `batch_sampler`: no_duplicates
|
|
- `multi_dataset_batch_sampler`: proportional
|
|
|
|
</details>
|
|
|
|
### Training Logs
|
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
|
|
| 0.8122 | 10 | 1.5267 | - | - | - | - | - |
|
|
| 0.9746 | 12 | - | 0.7446 | 0.7639 | 0.7765 | 0.7039 | 0.7725 |
|
|
| 1.6244 | 20 | 0.6742 | - | - | - | - | - |
|
|
| 1.9492 | 24 | - | 0.7606 | 0.7795 | 0.7828 | 0.7297 | 0.7839 |
|
|
| 2.4365 | 30 | 0.4469 | - | - | - | - | - |
|
|
| **2.9239** | **36** | **-** | **0.7643** | **0.7758** | **0.7834** | **0.7332** | **0.7845** |
|
|
| 3.2487 | 40 | 0.3712 | - | - | - | - | - |
|
|
| 3.8985 | 48 | - | 0.7609 | 0.7759 | 0.7832 | 0.7331 | 0.7867 |
|
|
|
|
* The bold row denotes the saved checkpoint.
|
|
|
|
### Framework Versions
|
|
- Python: 3.11.8
|
|
- Sentence Transformers: 3.0.1
|
|
- Transformers: 4.41.2
|
|
- PyTorch: 2.3.1+cu121
|
|
- Accelerate: 0.30.1
|
|
- 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}
|
|
}
|
|
```
|
|
|
|
<!--
|
|
## Glossary
|
|
|
|
*Clearly define terms in order to be accessible across audiences.*
|
|
-->
|
|
|
|
<!--
|
|
## Model Card Authors
|
|
|
|
*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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|
-->
|
|
|
|
<!--
|
|
## Model Card Contact
|
|
|
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
|
--> |
