|
--- |
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base_model: BAAI/bge-base-en-v1.5 |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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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 |
|
- cosine_precision@10 |
|
- 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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pipeline_tag: sentence-similarity |
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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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widget: |
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- source_sentence: The consolidated financial statements and accompanying notes listed |
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in Part IV, Item 15(a)(1) of this Annual Report on Form 10-K are included elsewhere |
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in this Annual Report on Form 10-K. |
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sentences: |
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- What is the carrying value of the indefinite-lived intangible assets related to |
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the Certificate of Needs and Medicare licenses as of December 31, 2023? |
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- What sections of the Annual Report on Form 10-K contain the company's financial |
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statements? |
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- What was the effective tax rate excluding discrete net tax benefits for the year |
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2022? |
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- source_sentence: Consumers are served through Amazon's online and physical stores |
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with an emphasis on selection, price, and convenience. |
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sentences: |
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- What decision did the European Commission make on July 10, 2023 regarding the |
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United States? |
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- What are the primary offerings to consumers through Amazon's online and physical |
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stores? |
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- What activities are included in the services and other revenue segment of General |
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Motors Company? |
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- source_sentence: Visa has traditionally referred to their structure of facilitating |
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secure, reliable, and efficient money movement among consumers, issuing and acquiring |
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financial institutions, and merchants as the 'four-party' model. |
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sentences: |
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- What model does Visa traditionally refer to regarding their transaction process |
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among consumers, financial institutions, and merchants? |
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- What percentage of Meta's U.S. workforce in 2023 were represented by people with |
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disabilities, veterans, and members of the LGBTQ+ community? |
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- What are the revenue sources for the Company’s Health Care Benefits Segment? |
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- source_sentence: 'In addition to LinkedIn’s free services, LinkedIn offers monetized |
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solutions: Talent Solutions, Marketing Solutions, Premium Subscriptions, and Sales |
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Solutions. Talent Solutions provide insights for workforce planning and tools |
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to hire, nurture, and develop talent. Talent Solutions also includes Learning |
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Solutions, which help businesses close critical skills gaps in times where companies |
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are having to do more with existing talent.' |
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sentences: |
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- What were the major factors contributing to the increased expenses excluding interest |
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for Investor Services and Advisor Services in 2023? |
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- What were the pre-tax earnings of the manufacturing sector in 2023, 2022, and |
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2021? |
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- What does LinkedIn's Talent Solutions include? |
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- source_sentence: Management assessed the effectiveness of the company’s internal |
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control over financial reporting as of December 31, 2023. In making this assessment, |
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we used the criteria set forth by the Committee of Sponsoring Organizations of |
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the Treadway Commission (COSO) in Internal Control—Integrated Framework (2013). |
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sentences: |
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- What criteria did Caterpillar Inc. use to assess the effectiveness of its internal |
|
control over financial reporting as of December 31, 2023? |
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- What are the primary components of U.S. sales volumes for Ford? |
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- What was the percentage increase in Schwab's common stock dividend in 2022? |
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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: 0.69 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
|
value: 0.8385714285714285 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
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value: 0.87 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.92 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.69 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
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value: 0.27952380952380956 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
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value: 0.174 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09199999999999998 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.69 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8385714285714285 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.87 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.92 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8078047173747194 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7717607709750567 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7745029834237301 |
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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: |
|
- type: cosine_accuracy@1 |
|
value: 0.7014285714285714 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8342857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8671428571428571 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9171428571428571 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7014285714285714 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.27809523809523806 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1734285714285714 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09171428571428569 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7014285714285714 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8342857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8671428571428571 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9171428571428571 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8099294101814819 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.775592970521542 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7785490266159816 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6928571428571428 |
|
name: Cosine Accuracy@1 |
|
- 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: 0.91 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6928571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2761904761904762 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17228571428571426 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.091 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6928571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8285714285714286 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8614285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.91 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8023495466461429 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7679013605442175 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7712468743892164 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6728571428571428 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.85 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8828571428571429 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6728571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2723809523809524 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16999999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08828571428571429 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6728571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.85 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8828571428571429 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7823204493781594 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7495634920634917 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.75425425293366 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.64 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.79 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.83 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8742857142857143 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.64 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26333333333333336 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16599999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08742857142857141 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.64 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.79 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.83 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8742857142857143 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7602361447545036 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7233747165532877 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7278552309882971 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base Financial Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
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- json |
|
- **Language:** en |
|
- **License:** apache-2.0 |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(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}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("sh4796/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
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'Management assessed the effectiveness of the company’s internal control over financial reporting as of December 31, 2023. In making this assessment, we used the criteria set forth by the Committee of Sponsoring Organizations of the Treadway Commission (COSO) in Internal Control—Integrated Framework (2013).', |
|
'What criteria did Caterpillar Inc. use to assess the effectiveness of its internal control over financial reporting as of December 31, 2023?', |
|
'What are the primary components of U.S. sales volumes for Ford?', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
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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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</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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### 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 |
|
|
|
### Metrics |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_768` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.69 | |
|
| cosine_accuracy@3 | 0.8386 | |
|
| cosine_accuracy@5 | 0.87 | |
|
| cosine_accuracy@10 | 0.92 | |
|
| cosine_precision@1 | 0.69 | |
|
| cosine_precision@3 | 0.2795 | |
|
| cosine_precision@5 | 0.174 | |
|
| cosine_precision@10 | 0.092 | |
|
| cosine_recall@1 | 0.69 | |
|
| cosine_recall@3 | 0.8386 | |
|
| cosine_recall@5 | 0.87 | |
|
| cosine_recall@10 | 0.92 | |
|
| cosine_ndcg@10 | 0.8078 | |
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| cosine_mrr@10 | 0.7718 | |
|
| **cosine_map@100** | **0.7745** | |
|
|
|
#### 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 | 0.7014 | |
|
| cosine_accuracy@3 | 0.8343 | |
|
| cosine_accuracy@5 | 0.8671 | |
|
| cosine_accuracy@10 | 0.9171 | |
|
| cosine_precision@1 | 0.7014 | |
|
| cosine_precision@3 | 0.2781 | |
|
| cosine_precision@5 | 0.1734 | |
|
| cosine_precision@10 | 0.0917 | |
|
| cosine_recall@1 | 0.7014 | |
|
| cosine_recall@3 | 0.8343 | |
|
| cosine_recall@5 | 0.8671 | |
|
| cosine_recall@10 | 0.9171 | |
|
| cosine_ndcg@10 | 0.8099 | |
|
| cosine_mrr@10 | 0.7756 | |
|
| **cosine_map@100** | **0.7785** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.6929 | |
|
| cosine_accuracy@3 | 0.8286 | |
|
| cosine_accuracy@5 | 0.8614 | |
|
| cosine_accuracy@10 | 0.91 | |
|
| cosine_precision@1 | 0.6929 | |
|
| cosine_precision@3 | 0.2762 | |
|
| cosine_precision@5 | 0.1723 | |
|
| cosine_precision@10 | 0.091 | |
|
| cosine_recall@1 | 0.6929 | |
|
| cosine_recall@3 | 0.8286 | |
|
| cosine_recall@5 | 0.8614 | |
|
| cosine_recall@10 | 0.91 | |
|
| cosine_ndcg@10 | 0.8023 | |
|
| cosine_mrr@10 | 0.7679 | |
|
| **cosine_map@100** | **0.7712** | |
|
|
|
#### 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 | 0.6729 | |
|
| cosine_accuracy@3 | 0.8171 | |
|
| cosine_accuracy@5 | 0.85 | |
|
| cosine_accuracy@10 | 0.8829 | |
|
| cosine_precision@1 | 0.6729 | |
|
| cosine_precision@3 | 0.2724 | |
|
| cosine_precision@5 | 0.17 | |
|
| cosine_precision@10 | 0.0883 | |
|
| cosine_recall@1 | 0.6729 | |
|
| cosine_recall@3 | 0.8171 | |
|
| cosine_recall@5 | 0.85 | |
|
| cosine_recall@10 | 0.8829 | |
|
| cosine_ndcg@10 | 0.7823 | |
|
| cosine_mrr@10 | 0.7496 | |
|
| **cosine_map@100** | **0.7543** | |
|
|
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#### Information Retrieval |
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* Dataset: `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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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.64 | |
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| cosine_accuracy@3 | 0.79 | |
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| cosine_accuracy@5 | 0.83 | |
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| cosine_accuracy@10 | 0.8743 | |
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| cosine_precision@1 | 0.64 | |
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| cosine_precision@3 | 0.2633 | |
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| cosine_precision@5 | 0.166 | |
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| cosine_precision@10 | 0.0874 | |
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| cosine_recall@1 | 0.64 | |
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| cosine_recall@3 | 0.79 | |
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| cosine_recall@5 | 0.83 | |
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| cosine_recall@10 | 0.8743 | |
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| cosine_ndcg@10 | 0.7602 | |
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| cosine_mrr@10 | 0.7234 | |
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| **cosine_map@100** | **0.7279** | |
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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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### Recommendations |
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## Training Details |
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|
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### Training Dataset |
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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: 8 tokens</li><li>mean: 44.33 tokens</li><li>max: 289 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.43 tokens</li><li>max: 46 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>The Company defines fair value as the price received to transfer an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date. In accordance with ASC 820, Fair Value Measurements and Disclosures, the Company uses the fair value hierarchy which prioritizes the inputs used to measure fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1), observable inputs other than quoted prices (Level 2), and unobservable inputs (Level 3).</code> | <code>What is the role of Level 1, Level 2, and Level 3 inputs in the fair value hierarchy according to ASC 820?</code> | |
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| <code>In the event of conversion of the Notes, if shares are delivered to the Company under the Capped Call Transactions, they will offset the dilutive effect of the shares that the Company would issue under the Notes.</code> | <code>What happens to the dilutive effect of shares issued under the Notes if shares are delivered to the Company under the Capped Call Transactions during the conversion?</code> | |
|
| <code>Marketing expenses increased $48.8 million to $759.2 million in the year ended December 31, 2023 compared to the year ended December 31, 2022.</code> | <code>How much did the marketing expenses increase in the year ended December 31, 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", |
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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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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
|
- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.8122 | 10 | 1.5604 | - | - | - | - | - | |
|
| 0.9746 | 12 | - | 0.7538 | 0.7540 | 0.7483 | 0.7284 | 0.6906 | |
|
| 1.6244 | 20 | 0.6618 | - | - | - | - | - | |
|
| 1.9492 | 24 | - | 0.7654 | 0.7632 | 0.7582 | 0.7424 | 0.7186 | |
|
| 2.4365 | 30 | 0.4579 | - | - | - | - | - | |
|
| 2.9239 | 36 | - | 0.7686 | 0.7646 | 0.7619 | 0.7459 | 0.7238 | |
|
| 3.2487 | 40 | 0.3995 | - | - | - | - | - | |
|
| **3.8985** | **48** | **-** | **0.7694** | **0.7633** | **0.7641** | **0.7449** | **0.7225** | |
|
| 0.8122 | 10 | 0.3798 | - | - | - | - | - | |
|
| 0.9746 | 12 | - | 0.7713 | 0.7685 | 0.7691 | 0.7489 | 0.7249 | |
|
| 1.6244 | 20 | 0.2958 | - | - | - | - | - | |
|
| 1.9492 | 24 | - | 0.7726 | 0.7699 | 0.7688 | 0.7517 | 0.7283 | |
|
| 2.4365 | 30 | 0.2273 | - | - | - | - | - | |
|
| 2.9239 | 36 | - | 0.7742 | 0.7761 | 0.7734 | 0.7532 | 0.7276 | |
|
| 3.2487 | 40 | 0.2136 | - | - | - | - | - | |
|
| **3.8985** | **48** | **-** | **0.7745** | **0.7785** | **0.7712** | **0.7543** | **0.7279** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.2.0 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.2.0a0+6a974be |
|
- Accelerate: 0.27.0 |
|
- 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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