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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: 'The Innovative Medicine segment is focused on the following therapeutic
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areas: Immunology, Infectious diseases, Neuroscience, Oncology, Pulmonary Hypertension,
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and Cardiovascular and Metabolic diseases.'
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sentences:
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- What was the primary reason for the decrease in adjusted operating income in 2023?
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- What therapeutic areas does the Innovative Medicine segment of Johnson & Johnson
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focus on?
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- What was the remaining budget for the September 2022 Repurchase Program as of
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January 28, 2023?
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- source_sentence: It may be necessary in the future to seek or renew licenses relating
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to various aspects of the Company’s products, processes and services. While the
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Company has generally been able to obtain such licenses on commercially reasonable
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terms in the past, there is no guarantee that such licenses could be obtained
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in the future on reasonable terms or at all.
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sentences:
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- What was the percentage change in total earning assets from the previous year
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as reported in 2023?
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- What is Apple's approach to licenses for intellectual property owned by third
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parties used in its products and services?
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- Why did the Ontario class action related to the 2017 cybersecurity incident progress
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differently than other cases?
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- source_sentence: Assets and liabilities measured at fair value on a nonrecurring
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basis in the consolidated financial statements include items such as property,
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plant and equipment, ROU assets, goodwill and other intangible assets, equity
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and other investments and other assets. These are measured at fair value if determined
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to be impaired.
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sentences:
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- How are assets and liabilities that are measured at fair value on a nonrecurring
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basis identified in the financial statements?
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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 diversity and inclusion goals has Goldman Sachs set for its workforce by
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2025?
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- source_sentence: Based on management’s allocation decision, the portion of the Credit
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Facility available to ME&T as of December 31, 2023 was $2.75 billion.
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sentences:
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- What were the components of the increase in costs related to operating channels
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in 2023?
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- What are the goals of American Express’s balance sheet management strategy?
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- How much of the Credit Facility was available to ME&T as of December 31, 2023?
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- source_sentence: Item 8 is labeled 'Financial Statements and Supplementary Data.'
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sentences:
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- What section of the document is labeled 'Item 8'?
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- For comprehensive information on a company's legal matters, which part of the
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financial statement should one consult?
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- What was the return on average common stockholders’ equity for 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: 0.6928571428571428
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name: Cosine Accuracy@1
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- type: cosine_accuracy@3
|
|
value: 0.8257142857142857
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|
name: Cosine Accuracy@3
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|
- type: cosine_accuracy@5
|
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value: 0.8671428571428571
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|
name: Cosine Accuracy@5
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- type: cosine_accuracy@10
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value: 0.9071428571428571
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name: Cosine Accuracy@10
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- type: cosine_precision@1
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value: 0.6928571428571428
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name: Cosine Precision@1
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- type: cosine_precision@3
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value: 0.2752380952380953
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name: Cosine Precision@3
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- type: cosine_precision@5
|
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value: 0.1734285714285714
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|
name: Cosine Precision@5
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- type: cosine_precision@10
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value: 0.0907142857142857
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name: Cosine Precision@10
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- type: cosine_recall@1
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value: 0.6928571428571428
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name: Cosine Recall@1
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- type: cosine_recall@3
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value: 0.8257142857142857
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|
name: Cosine Recall@3
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- type: cosine_recall@5
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value: 0.8671428571428571
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|
name: Cosine Recall@5
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- type: cosine_recall@10
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value: 0.9071428571428571
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|
name: Cosine Recall@10
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- type: cosine_ndcg@10
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value: 0.8015678007585516
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|
name: Cosine Ndcg@10
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|
- type: cosine_mrr@10
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value: 0.7675442176870747
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|
name: Cosine Mrr@10
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- type: cosine_map@100
|
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value: 0.7711683558124478
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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: 0.6914285714285714
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|
name: Cosine Accuracy@1
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- type: cosine_accuracy@3
|
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value: 0.8257142857142857
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|
name: Cosine Accuracy@3
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- type: cosine_accuracy@5
|
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value: 0.8671428571428571
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name: Cosine Accuracy@5
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- type: cosine_accuracy@10
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value: 0.9071428571428571
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name: Cosine Accuracy@10
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- type: cosine_precision@1
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value: 0.6914285714285714
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name: Cosine Precision@1
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- type: cosine_precision@3
|
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value: 0.2752380952380952
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name: Cosine Precision@3
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- type: cosine_precision@5
|
|
value: 0.1734285714285714
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|
name: Cosine Precision@5
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- type: cosine_precision@10
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value: 0.0907142857142857
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name: Cosine Precision@10
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- type: cosine_recall@1
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value: 0.6914285714285714
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name: Cosine Recall@1
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- type: cosine_recall@3
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value: 0.8257142857142857
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|
name: Cosine Recall@3
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- type: cosine_recall@5
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value: 0.8671428571428571
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|
name: Cosine Recall@5
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- type: cosine_recall@10
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value: 0.9071428571428571
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name: Cosine Recall@10
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- type: cosine_ndcg@10
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value: 0.8009375601369785
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name: Cosine Ndcg@10
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- type: cosine_mrr@10
|
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value: 0.7666672335600906
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name: Cosine Mrr@10
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- type: cosine_map@100
|
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value: 0.7701113420260945
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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: 0.6871428571428572
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|
name: Cosine Accuracy@1
|
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- type: cosine_accuracy@3
|
|
value: 0.8242857142857143
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|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.8585714285714285
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.9028571428571428
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.6871428571428572
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.2747619047619047
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|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.1717142857142857
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.09028571428571427
|
|
name: Cosine Precision@10
|
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- type: cosine_recall@1
|
|
value: 0.6871428571428572
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.8242857142857143
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.8585714285714285
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.9028571428571428
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.7965630325935761
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.7623344671201813
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.7659656636117955
|
|
name: Cosine Map@100
|
|
- task:
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type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
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name: dim 128
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type: dim_128
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|
metrics:
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- type: cosine_accuracy@1
|
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value: 0.6728571428571428
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.8085714285714286
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.8485714285714285
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.8871428571428571
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.6728571428571428
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.26952380952380944
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.16971428571428568
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.0887142857142857
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.6728571428571428
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.8085714285714286
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.8485714285714285
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.8871428571428571
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.7820355932651222
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.7480856009070294
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.7523134135641188
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: dim 64
|
|
type: dim_64
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.6357142857142857
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.7685714285714286
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.8128571428571428
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.86
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.6357142857142857
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.2561904761904762
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.16257142857142853
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.086
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.6357142857142857
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.7685714285714286
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.8128571428571428
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.86
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.7472648621107045
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.7111729024943308
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.7168773691247933
|
|
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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|
|
### Model Description
|
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- **Model Type:** Sentence Transformer
|
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
|
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- **Maximum Sequence Length:** 512 tokens
|
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- **Output Dimensionality:** 768 dimensions
|
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- **Similarity Function:** Cosine Similarity
|
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- **Training Dataset:**
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- json
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- **Language:** en
|
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- **License:** apache-2.0
|
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|
|
### Model Sources
|
|
|
|
- **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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|
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```
|
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SentenceTransformer(
|
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
|
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
|
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(2): Normalize()
|
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)
|
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```
|
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|
|
## 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("schawla2/bge-base-financial-matryoshka")
|
|
# Run inference
|
|
sentences = [
|
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"Item 8 is labeled 'Financial Statements and Supplementary Data.'",
|
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"What section of the document is labeled 'Item 8'?",
|
|
"For comprehensive information on a company's legal matters, which part of the financial statement should one consult?",
|
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]
|
|
embeddings = model.encode(sentences)
|
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print(embeddings.shape)
|
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# [3, 768]
|
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|
|
# Get the similarity scores for the embeddings
|
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similarities = model.similarity(embeddings, embeddings)
|
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print(similarities.shape)
|
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# [3, 3]
|
|
```
|
|
|
|
<!--
|
|
### 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.
|
|
|
|
<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
|
|
|
|
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
|
|
|
|
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|
|
|:--------------------|:-----------|:-----------|:-----------|:----------|:-----------|
|
|
| cosine_accuracy@1 | 0.6929 | 0.6914 | 0.6871 | 0.6729 | 0.6357 |
|
|
| cosine_accuracy@3 | 0.8257 | 0.8257 | 0.8243 | 0.8086 | 0.7686 |
|
|
| cosine_accuracy@5 | 0.8671 | 0.8671 | 0.8586 | 0.8486 | 0.8129 |
|
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| cosine_accuracy@10 | 0.9071 | 0.9071 | 0.9029 | 0.8871 | 0.86 |
|
|
| cosine_precision@1 | 0.6929 | 0.6914 | 0.6871 | 0.6729 | 0.6357 |
|
|
| cosine_precision@3 | 0.2752 | 0.2752 | 0.2748 | 0.2695 | 0.2562 |
|
|
| cosine_precision@5 | 0.1734 | 0.1734 | 0.1717 | 0.1697 | 0.1626 |
|
|
| cosine_precision@10 | 0.0907 | 0.0907 | 0.0903 | 0.0887 | 0.086 |
|
|
| cosine_recall@1 | 0.6929 | 0.6914 | 0.6871 | 0.6729 | 0.6357 |
|
|
| cosine_recall@3 | 0.8257 | 0.8257 | 0.8243 | 0.8086 | 0.7686 |
|
|
| cosine_recall@5 | 0.8671 | 0.8671 | 0.8586 | 0.8486 | 0.8129 |
|
|
| cosine_recall@10 | 0.9071 | 0.9071 | 0.9029 | 0.8871 | 0.86 |
|
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| **cosine_ndcg@10** | **0.8016** | **0.8009** | **0.7966** | **0.782** | **0.7473** |
|
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| cosine_mrr@10 | 0.7675 | 0.7667 | 0.7623 | 0.7481 | 0.7112 |
|
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| cosine_map@100 | 0.7712 | 0.7701 | 0.766 | 0.7523 | 0.7169 |
|
|
|
|
<!--
|
|
## Bias, Risks and Limitations
|
|
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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
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### Training Dataset
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#### json
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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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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
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| type | string | string |
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| details | <ul><li>min: 4 tokens</li><li>mean: 45.97 tokens</li><li>max: 326 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.57 tokens</li><li>max: 46 tokens</li></ul> |
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* Samples:
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| positive | anchor |
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|:----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|
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| <code>In 2023, Delta took delivery of 43 aircraft.</code> | <code>How many new aircraft did Delta Air Lines take delivery of in 2023?</code> |
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| <code>Item 8 incorporates pages 44 through 121 of IBM’s 2023 Annual Report to Stockholders by reference.</code> | <code>What sections of IBM's 2023 Annual Report are incorporated into Item 8 of the Form 10-K?</code> |
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| <code>Total borrowings at the end of 2023 were $29.3 billion.</code> | <code>What was the total amount of debt the Company had at the end 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",
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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
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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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#### 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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- `torch_empty_cache_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
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- `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`: None
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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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- `include_for_metrics`: []
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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
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- `torch_compile_backend`: None
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- `torch_compile_mode`: None
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- `dispatch_batches`: None
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- `split_batches`: None
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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: False
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- `neftune_noise_alpha`: None
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- `optim_target_modules`: None
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- `batch_eval_metrics`: False
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- `eval_on_start`: False
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- `use_liger_kernel`: False
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- `eval_use_gather_object`: False
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- `average_tokens_across_devices`: False
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- `prompts`: None
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- `batch_sampler`: no_duplicates
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- `multi_dataset_batch_sampler`: proportional
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</details>
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### Training Logs
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| 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 |
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|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
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| 0.8122 | 10 | 24.5441 | - | - | - | - | - |
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| 1.0 | 13 | - | 0.7920 | 0.7932 | 0.7878 | 0.7699 | 0.7305 |
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| 1.5685 | 20 | 10.1942 | - | - | - | - | - |
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| 2.0 | 26 | - | 0.7998 | 0.7988 | 0.7968 | 0.7818 | 0.7443 |
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| 2.3249 | 30 | 6.8787 | - | - | - | - | - |
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| **3.0** | **39** | **-** | **0.8014** | **0.8015** | **0.7976** | **0.7825** | **0.7503** |
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| 3.0812 | 40 | 6.1782 | - | - | - | - | - |
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| 3.7310 | 48 | - | 0.8016 | 0.8009 | 0.7966 | 0.7820 | 0.7473 |
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* The bold row denotes the saved checkpoint.
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### Framework Versions
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- Python: 3.10.16
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- Sentence Transformers: 3.3.1
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- Transformers: 4.48.1
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- PyTorch: 2.5.1+cu124
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- Accelerate: 1.3.0
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- Datasets: 3.3.2
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- Tokenizers: 0.21.0
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## Citation
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### BibTeX
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#### Sentence Transformers
|
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```bibtex
|
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@inproceedings{reimers-2019-sentence-bert,
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2019",
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publisher = "Association for Computational Linguistics",
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url = "https://arxiv.org/abs/1908.10084",
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}
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```
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#### MatryoshkaLoss
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```bibtex
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@misc{kusupati2024matryoshka,
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title={Matryoshka Representation Learning},
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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},
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year={2024},
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eprint={2205.13147},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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```
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#### MultipleNegativesRankingLoss
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```bibtex
|
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@misc{henderson2017efficient,
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title={Efficient Natural Language Response Suggestion for Smart Reply},
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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},
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year={2017},
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eprint={1705.00652},
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archivePrefix={arXiv},
|
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primaryClass={cs.CL}
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}
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```
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