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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6300 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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widget: |
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- source_sentence: In 2023, total government-based programs, including Medicare, Medicaid, |
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and other government-based programs, contributed 67% to the U.S. dialysis patient |
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service revenues. |
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sentences: |
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- How does Iron Mountain's reported EPS fully diluted from net income in 2023 compare |
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to 2022? |
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- What was the total percentage of U.S. dialysis patient service revenues coming |
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from government-based programs in 2023? |
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- What year did the company introduce multiplex theatres? |
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- source_sentence: The gross realized losses on sales of AFS debt associated for 2023 |
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amounted to $514 million, indicating a negative financial outcome from these transactions |
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during the year. |
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sentences: |
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- What were the gross realized losses on sales of AFS debt securities in 2023? |
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- How is information about legal proceedings described in the Annual Report on Form |
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10-K? |
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- What sections are included alongside the Financial Statements in this report? |
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- source_sentence: Other income, net, changed favorably by $215 million in the year |
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ended December 31, 2023 as compared to the year ended December 31, 2022. The favorable |
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change was primarily due to fluctuations in foreign currency exchange rates on |
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our intercompany balances. |
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sentences: |
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- What was the monetary change in other income (expense), net, from 2022 to 2023? |
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- What strategic actions has Walmart International taken over the last three years? |
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- What is described under Item 8 in the context of a financial document? |
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- source_sentence: Segments The Company manages its business primarily on a geographic |
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basis. The Company’s reportable segments consist of the Americas, Europe, Greater |
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China, Japan and Rest of Asia Pacific. |
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sentences: |
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- What is the total debt repayment obligation mentioned in the financial outline? |
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- What segments does the Company manage its business on? |
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- What is the title of Item 8 which contains page information in a financial document? |
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- source_sentence: Item 8 typically refers to Financial Statements and Supplementary |
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Data in a document. |
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sentences: |
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- What is the primary function of Etsy's online marketplaces? |
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- What are the maximum leverage ratios specified under the Senior Credit Facilities |
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for the periods ending fourth quarter of 2023 and first quarter of 2024? |
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- What does Item 8 in a document usually represent? |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7057142857142857 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8371428571428572 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8742857142857143 |
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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: 0.7057142857142857 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27904761904761904 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17485714285714282 |
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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: 0.7057142857142857 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8371428571428572 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8742857142857143 |
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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: 0.8114149232737874 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7786632653061224 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7821804400415905 |
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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: 0.7057142857142857 |
|
name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8328571428571429 |
|
name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
|
value: 0.8714285714285714 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
|
value: 0.9128571428571428 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7057142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2776190476190476 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17428571428571427 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09128571428571428 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
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value: 0.7057142857142857 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8328571428571429 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8714285714285714 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9128571428571428 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8108495475926208 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7780068027210884 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7816465534941897 |
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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: 0.7157142857142857 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8342857142857143 |
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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: 0.9057142857142857 |
|
name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
|
value: 0.7157142857142857 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27809523809523806 |
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name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.174 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
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value: 0.09057142857142855 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
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value: 0.7157142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8342857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.87 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9057142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8123157823677117 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7823004535147391 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7862892219643212 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
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name: Information Retrieval |
|
dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
|
- type: cosine_accuracy@1 |
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value: 0.6928571428571428 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8614285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9028571428571428 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6928571428571428 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2723809523809524 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17228571428571426 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09028571428571427 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6928571428571428 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8614285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9028571428571428 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7975011441256048 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7638248299319729 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7673061455577762 |
|
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). 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 |
|
|
|
### 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 |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
- **Language:** en |
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- **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("pavanmantha/bge-base-en-honsec10k-embed") |
|
# Run inference |
|
sentences = [ |
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'Item 8 typically refers to Financial Statements and Supplementary Data in a document.', |
|
'What does Item 8 in a document usually represent?', |
|
'What are the maximum leverage ratios specified under the Senior Credit Facilities for the periods ending fourth quarter of 2023 and first quarter of 2024?', |
|
] |
|
embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
|
|
|
</details> |
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--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
|
|
|
You can finetune this model on your own dataset. |
|
|
|
<details><summary>Click to expand</summary> |
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|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### 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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--> |
|
|
|
## Evaluation |
|
|
|
### Metrics |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_768` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.7057 | |
|
| cosine_accuracy@3 | 0.8371 | |
|
| cosine_accuracy@5 | 0.8743 | |
|
| cosine_accuracy@10 | 0.9129 | |
|
| cosine_precision@1 | 0.7057 | |
|
| cosine_precision@3 | 0.279 | |
|
| cosine_precision@5 | 0.1749 | |
|
| cosine_precision@10 | 0.0913 | |
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| cosine_recall@1 | 0.7057 | |
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| cosine_recall@3 | 0.8371 | |
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| cosine_recall@5 | 0.8743 | |
|
| cosine_recall@10 | 0.9129 | |
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| cosine_ndcg@10 | 0.8114 | |
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| cosine_mrr@10 | 0.7787 | |
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| **cosine_map@100** | **0.7822** | |
|
|
|
#### 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 | 0.7057 | |
|
| cosine_accuracy@3 | 0.8329 | |
|
| cosine_accuracy@5 | 0.8714 | |
|
| cosine_accuracy@10 | 0.9129 | |
|
| cosine_precision@1 | 0.7057 | |
|
| cosine_precision@3 | 0.2776 | |
|
| cosine_precision@5 | 0.1743 | |
|
| cosine_precision@10 | 0.0913 | |
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| cosine_recall@1 | 0.7057 | |
|
| cosine_recall@3 | 0.8329 | |
|
| cosine_recall@5 | 0.8714 | |
|
| cosine_recall@10 | 0.9129 | |
|
| cosine_ndcg@10 | 0.8108 | |
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| cosine_mrr@10 | 0.778 | |
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| **cosine_map@100** | **0.7816** | |
|
|
|
#### 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 | 0.7157 | |
|
| cosine_accuracy@3 | 0.8343 | |
|
| cosine_accuracy@5 | 0.87 | |
|
| cosine_accuracy@10 | 0.9057 | |
|
| cosine_precision@1 | 0.7157 | |
|
| cosine_precision@3 | 0.2781 | |
|
| cosine_precision@5 | 0.174 | |
|
| cosine_precision@10 | 0.0906 | |
|
| cosine_recall@1 | 0.7157 | |
|
| cosine_recall@3 | 0.8343 | |
|
| cosine_recall@5 | 0.87 | |
|
| cosine_recall@10 | 0.9057 | |
|
| cosine_ndcg@10 | 0.8123 | |
|
| cosine_mrr@10 | 0.7823 | |
|
| **cosine_map@100** | **0.7863** | |
|
|
|
#### 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 | 0.6929 | |
|
| cosine_accuracy@3 | 0.8171 | |
|
| cosine_accuracy@5 | 0.8614 | |
|
| cosine_accuracy@10 | 0.9029 | |
|
| cosine_precision@1 | 0.6929 | |
|
| cosine_precision@3 | 0.2724 | |
|
| cosine_precision@5 | 0.1723 | |
|
| cosine_precision@10 | 0.0903 | |
|
| cosine_recall@1 | 0.6929 | |
|
| cosine_recall@3 | 0.8171 | |
|
| cosine_recall@5 | 0.8614 | |
|
| cosine_recall@10 | 0.9029 | |
|
| cosine_ndcg@10 | 0.7975 | |
|
| cosine_mrr@10 | 0.7638 | |
|
| **cosine_map@100** | **0.7673** | |
|
|
|
<!-- |
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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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<!-- |
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### Recommendations |
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|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 6,300 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 6 tokens</li><li>mean: 44.43 tokens</li><li>max: 248 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.52 tokens</li><li>max: 45 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------| |
|
| <code>Net deferred tax liabilities | $ | (304) | | | $ | (279) The deferred tax accounts at the end of 2023 and 2022 include deferred income tax assets of $491 and $445, included in other long-term assets; and deferred income tax liabilities of $795 and $724, included in other long-term liabilities.</code> | <code>What are the net deferred tax liabilities for the company at the end of 2023?</code> | |
|
| <code>ITEM 3. LEGAL PROCEEDINGS Please see the legal proceedings described in Note 21. Commitments and Contingencies included in Item 8 of Part II of this report.</code> | <code>In what part and item of the report is Note 21 located?</code> | |
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| <code>During fiscal year 2023, we repurchased 10.4 million shares for approximately $1,295 million.</code> | <code>What total amount was spent on share repurchases during fiscal year 2023?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### 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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- `fp16`: True |
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- `tf32`: False |
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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`: False |
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- `fp16`: True |
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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`: False |
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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`: 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 |
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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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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| |
|
| 0.8122 | 10 | 1.1537 | - | - | - | - | |
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| 0.9746 | 12 | - | 0.7517 | 0.7620 | 0.7633 | 0.7636 | |
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| 1.6244 | 20 | 0.4387 | - | - | - | - | |
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| 1.9492 | 24 | - | 0.7616 | 0.7802 | 0.7796 | 0.7769 | |
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| 2.4365 | 30 | 0.3113 | - | - | - | - | |
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| 2.9239 | 36 | - | 0.7668 | 0.7837 | 0.7809 | 0.7821 | |
|
| 3.2487 | 40 | 0.2554 | - | - | - | - | |
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| **3.8985** | **48** | **-** | **0.7673** | **0.7863** | **0.7816** | **0.7822** | |
|
|
|
* The bold row denotes the saved checkpoint. |
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|
|
### Framework Versions |
|
- Python: 3.10.13 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.1.2 |
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- Accelerate: 0.31.0 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
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### BibTeX |
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|
|
#### Sentence Transformers |
|
```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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``` |
|
|
|
#### MatryoshkaLoss |
|
```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}, |
|
year={2024}, |
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eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
|
``` |
|
|
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#### MultipleNegativesRankingLoss |
|
```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}, |
|
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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