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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: nomic-ai/modernbert-embed-base |
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widget: |
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- source_sentence: HP reviews goodwill for impairment by initially performing a qualitative |
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assessment to see if the fair value of a reporting unit is likely less than its |
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carrying amount. If more likely, a quantitative assessment follows. |
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sentences: |
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- What percentage did the Communications segment account for of the 2023 total segment |
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income? |
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- How does HP determine whether goodwill impairment exists? |
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- What was the primary reason for the actuarial gain during the year ended December |
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31, 2022? |
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- source_sentence: The consolidated financial statements and accompanying notes are |
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listed in Part IV, Item 15(a)(1). |
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sentences: |
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- What does Item 8 in the Annual Report on Form 10-K detail? |
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- In which part of the Annual Report on Form 10-K are the consolidated financial |
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statements and accompanying notes listed? |
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- What is the estimated redemption rate for Chipotle gift cards? |
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- source_sentence: American Express maintains direct relationships with Card Members |
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and merchants, which provides it with direct access to information at both ends |
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of the transaction, distinguishing its integrated payments platform from the bankcard |
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networks. |
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sentences: |
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- How does American Express's integrated payments platform differentiate itself |
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from bankcard networks? |
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- How are contingent consideration liabilities valued? |
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- How does Chipotle calculate revenue recognition for redeemed Chipotle Rewards? |
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- source_sentence: Open Value agreements are a simple, cost-effective way to acquire |
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the latest Microsoft technology. These agreements are designed for small and medium |
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organizations that want to license cloud services and on-premises software over |
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a three-year period. Under Open Value agreements, organizations can elect to purchase |
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perpetual licenses or subscribe to licenses and SA is included. |
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sentences: |
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- How are unpaid losses and loss expenses calculated in the financial statements |
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of an insurance and reinsurance company? |
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- What type of financial documents are included in Part IV, Item 15(a)(1) of the |
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Annual Report on Form 10-K? |
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- What type of organizations is the Open Value agreements designed for and what |
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licenses does it include? |
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- source_sentence: The company's financial report indicates that the pre-tax amounts |
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of gains (losses) from foreign currency forward exchange contracts designated |
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as cash flow hedges were gains of $82 million in 2021, gains of $103 million in |
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2022, and losses of $2 million in 2023. |
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sentences: |
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- What were the pre-tax amounts of (gains) losses from foreign currency forward |
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exchange contracts designated as cash flow hedges for the years ended December |
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31 from 2021 to 2023? |
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- What is the projected change in income before income taxes if the 2023 discount |
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rate for the U.S. defined benefit pension and retiree health benefit plans changes |
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by a quarter percentage point? |
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- What sources contribute to Ford Credit’s liquidity as of December 31, 2023, and |
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what was their total value? |
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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: 0.6914285714285714 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8171428571428572 |
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name: Cosine Accuracy@3 |
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- 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: 0.9128571428571428 |
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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: 0.2723809523809524 |
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name: Cosine Precision@3 |
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- 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: 0.09128571428571428 |
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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: 0.8171428571428572 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.87 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9128571428571428 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8015002951126636 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7659410430839002 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.76947397245476 |
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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: 0.6642857142857143 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.81 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8557142857142858 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8971428571428571 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.6642857142857143 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17114285714285712 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.0897142857142857 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.6642857142857143 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.81 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8557142857142858 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8971428571428571 |
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|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7834209531598721 |
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|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7467698412698411 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7514515853623652 |
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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 64 |
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type: dim_64 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.62 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.7671428571428571 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8171428571428572 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8742857142857143 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.62 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2557142857142857 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1634285714285714 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.08742857142857142 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.62 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.7671428571428571 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8171428571428572 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8742857142857143 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7453405840762105 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7042613378684806 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.70911408987056 |
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name: Cosine Map@100 |
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--- |
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# BGE base Financial Matryoshka |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) 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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 --> |
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- **Maximum Sequence Length:** 8192 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 |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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 |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("Sorour/modernbert-financial-matryoshka") |
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# Run inference |
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sentences = [ |
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"The company's financial report indicates that the pre-tax amounts of gains (losses) from foreign currency forward exchange contracts designated as cash flow hedges were gains of $82 million in 2021, gains of $103 million in 2022, and losses of $2 million in 2023.", |
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'What were the pre-tax amounts of (gains) losses from foreign currency forward exchange contracts designated as cash flow hedges for the years ended December 31 from 2021 to 2023?', |
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'What is the projected change in income before income taxes if the 2023 discount rate for the U.S. defined benefit pension and retiree health benefit plans changes by a quarter percentage point?', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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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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### 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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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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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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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Datasets: `dim_768`, `dim_256` and `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | dim_768 | dim_256 | dim_64 | |
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|:--------------------|:-----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.6914 | 0.6643 | 0.62 | |
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| cosine_accuracy@3 | 0.8171 | 0.81 | 0.7671 | |
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| cosine_accuracy@5 | 0.87 | 0.8557 | 0.8171 | |
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| cosine_accuracy@10 | 0.9129 | 0.8971 | 0.8743 | |
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| cosine_precision@1 | 0.6914 | 0.6643 | 0.62 | |
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| cosine_precision@3 | 0.2724 | 0.27 | 0.2557 | |
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| cosine_precision@5 | 0.174 | 0.1711 | 0.1634 | |
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| cosine_precision@10 | 0.0913 | 0.0897 | 0.0874 | |
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| cosine_recall@1 | 0.6914 | 0.6643 | 0.62 | |
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| cosine_recall@3 | 0.8171 | 0.81 | 0.7671 | |
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| cosine_recall@5 | 0.87 | 0.8557 | 0.8171 | |
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| cosine_recall@10 | 0.9129 | 0.8971 | 0.8743 | |
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| **cosine_ndcg@10** | **0.8015** | **0.7834** | **0.7453** | |
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| cosine_mrr@10 | 0.7659 | 0.7468 | 0.7043 | |
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| cosine_map@100 | 0.7695 | 0.7515 | 0.7091 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## 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: 9 tokens</li><li>mean: 47.08 tokens</li><li>max: 998 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.19 tokens</li><li>max: 41 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------| |
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| <code>Item 8 includes Financial Statements and Supplementary Data.</code> | <code>What type of data is found in Item 8 of detailed financial documentation?</code> | |
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| <code>HP records revenue from the sale of equipment under sales-type leases as revenue at the commencement of the lease. This method is applied unless certain conditions such as customer acceptance remain uncertain or significant obligations to the customer remain unfulfilled.</code> | <code>How does HP recognize revenue from the sale of equipment under sales-type leases?</code> | |
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| <code>The company maintains insurance coverage for general liability, property, business interruption, terrorism, and other risks with respect to their business for all of their owned and leased hotels.</code> | <code>What types of risks are usually covered by the company's insurance policies?</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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256, |
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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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], |
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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 |
|
|
- `eval_accumulation_steps`: None |
|
|
- `torch_empty_cache_steps`: None |
|
|
- `learning_rate`: 2e-05 |
|
|
- `weight_decay`: 0.0 |
|
|
- `adam_beta1`: 0.9 |
|
|
- `adam_beta2`: 0.999 |
|
|
- `adam_epsilon`: 1e-08 |
|
|
- `max_grad_norm`: 1.0 |
|
|
- `num_train_epochs`: 4 |
|
|
- `max_steps`: -1 |
|
|
- `lr_scheduler_type`: cosine |
|
|
- `lr_scheduler_kwargs`: {} |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `warmup_steps`: 0 |
|
|
- `log_level`: passive |
|
|
- `log_level_replica`: warning |
|
|
- `log_on_each_node`: True |
|
|
- `logging_nan_inf_filter`: True |
|
|
- `save_safetensors`: True |
|
|
- `save_on_each_node`: False |
|
|
- `save_only_model`: False |
|
|
- `restore_callback_states_from_checkpoint`: False |
|
|
- `no_cuda`: False |
|
|
- `use_cpu`: False |
|
|
- `use_mps_device`: False |
|
|
- `seed`: 42 |
|
|
- `data_seed`: None |
|
|
- `jit_mode_eval`: False |
|
|
- `use_ipex`: False |
|
|
- `bf16`: True |
|
|
- `fp16`: False |
|
|
- `fp16_opt_level`: O1 |
|
|
- `half_precision_backend`: auto |
|
|
- `bf16_full_eval`: False |
|
|
- `fp16_full_eval`: False |
|
|
- `tf32`: True |
|
|
- `local_rank`: 0 |
|
|
- `ddp_backend`: None |
|
|
- `tpu_num_cores`: None |
|
|
- `tpu_metrics_debug`: False |
|
|
- `debug`: [] |
|
|
- `dataloader_drop_last`: False |
|
|
- `dataloader_num_workers`: 0 |
|
|
- `dataloader_prefetch_factor`: None |
|
|
- `past_index`: -1 |
|
|
- `disable_tqdm`: False |
|
|
- `remove_unused_columns`: True |
|
|
- `label_names`: None |
|
|
- `load_best_model_at_end`: True |
|
|
- `ignore_data_skip`: False |
|
|
- `fsdp`: [] |
|
|
- `fsdp_min_num_params`: 0 |
|
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
|
- `deepspeed`: None |
|
|
- `label_smoothing_factor`: 0.0 |
|
|
- `optim`: adamw_torch_fused |
|
|
- `optim_args`: None |
|
|
- `adafactor`: False |
|
|
- `group_by_length`: False |
|
|
- `length_column_name`: length |
|
|
- `ddp_find_unused_parameters`: None |
|
|
- `ddp_bucket_cap_mb`: None |
|
|
- `ddp_broadcast_buffers`: False |
|
|
- `dataloader_pin_memory`: True |
|
|
- `dataloader_persistent_workers`: False |
|
|
- `skip_memory_metrics`: True |
|
|
- `use_legacy_prediction_loop`: False |
|
|
- `push_to_hub`: False |
|
|
- `resume_from_checkpoint`: None |
|
|
- `hub_model_id`: None |
|
|
- `hub_strategy`: every_save |
|
|
- `hub_private_repo`: None |
|
|
- `hub_always_push`: False |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `include_for_metrics`: [] |
|
|
- `eval_do_concat_batches`: True |
|
|
- `fp16_backend`: auto |
|
|
- `push_to_hub_model_id`: None |
|
|
- `push_to_hub_organization`: None |
|
|
- `mp_parameters`: |
|
|
- `auto_find_batch_size`: False |
|
|
- `full_determinism`: False |
|
|
- `torchdynamo`: None |
|
|
- `ray_scope`: last |
|
|
- `ddp_timeout`: 1800 |
|
|
- `torch_compile`: False |
|
|
- `torch_compile_backend`: None |
|
|
- `torch_compile_mode`: None |
|
|
- `dispatch_batches`: None |
|
|
- `split_batches`: None |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|
|:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:---------------------:| |
|
|
| 0.8122 | 10 | 9.4544 | - | - | - | |
|
|
| 1.0 | 13 | - | 0.7799 | 0.7650 | 0.7097 | |
|
|
| 0.8122 | 10 | 3.1908 | - | - | - | |
|
|
| 1.0 | 13 | - | 0.7952 | 0.7769 | 0.7259 | |
|
|
| 1.5685 | 20 | 1.8807 | - | - | - | |
|
|
| 2.0 | 26 | - | 0.8001 | 0.7833 | 0.7409 | |
|
|
| 2.3249 | 30 | 1.7141 | - | - | - | |
|
|
| 3.0 | 39 | - | 0.8023 | 0.7819 | 0.7460 | |
|
|
| 3.0812 | 40 | 1.3672 | - | - | - | |
|
|
| **3.731** | **48** | **-** | **0.8015** | **0.7834** | **0.7453** | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 3.4.1 |
|
|
- Transformers: 4.48.3 |
|
|
- PyTorch: 2.5.1+cu124 |
|
|
- Accelerate: 1.3.0 |
|
|
- Datasets: 3.3.2 |
|
|
- Tokenizers: 0.21.0 |
|
|
|
|
|
## 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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