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--- |
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base_model: BAAI/bge-base-en-v1.5 |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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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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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6300 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: In the Annual Report on Form 10-K, the consolidated financial statements |
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are included immediately following Part IV and incorporated by reference. |
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sentences: |
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- What movies contributed to higher revenue in 2023 compared to the previous year? |
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- How are the financial statements incorporated in the 10-K report? |
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- What was the ending store count for the Family Dollar segment after the fiscal |
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year ended January 28, 2023? |
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- source_sentence: Readers are cautioned not to place undue reliance on forward-looking |
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statements, which speak only as of the date they are made. We undertake no obligation |
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to update or revise publicly any forward-looking statements, whether because of |
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new information, future events, or otherwise. |
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sentences: |
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- What impact did the IRS deadline extension in 2023 have on Intuit's fiscal results? |
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- What risks are associated with relying on forward-looking statements according |
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to the provided text? |
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- What were the total minimum lease payments and their net amounts after imputed |
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interest for operating and finance leases as of January 31, 2023? |
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- source_sentence: CMS made significant changes to the structure of the hierarchical |
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condition category model in version 28, which may impact risk adjustment factor |
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scores for a larger percentage of Medicare Advantage beneficiaries and could result |
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in changes to beneficiary RAF scores with or without a change in the patient’s |
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health status. |
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sentences: |
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- What significant regulatory change did CMS make to the hierarchical condition |
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category model in its version 28? |
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- Which section of IBM’s 2023 Annual Report is reserved for Financial Statements |
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and Supplementary Data? |
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- What strategic goals are set for the Printing segment at HP Inc.? |
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- source_sentence: In December 2023, the FCA published a consultation proposing to |
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revise the U.K. commodity derivatives framework. The FSMA 2023 reformed the U.K.’s |
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commodity derivatives regulatory regime including revoking the MIFID II position |
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limit requirements and transferring the powers to set position limits and controls |
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from the FCA to the operator of trading venues. The FCA proposal requires U.K. |
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trading venues to set position limits for critical and related contracts, to establish |
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accountability thresholds and to report enhanced position data. |
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sentences: |
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- What was the percentage increase in revenues from aviation services in 2023 compared |
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to 2022? |
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- What was the impairment loss recognized by the Company due to TDA integration |
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and restructuring efforts for the year ending December 31, 2023? |
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- What changes did the FCA propose in its December 2023 consultation regarding the |
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U.K. commodity derivatives framework? |
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- source_sentence: Operating cash flow provides the primary source of cash to fund |
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operating needs and capital expenditures. |
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sentences: |
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- What is the primary source of cash used by the company to fund operating needs |
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and capital expenditures? |
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- What kinds of products and services does the Company provide under the AARP Program? |
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- What was the total assets under supervision (AUS) for all categories combined |
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in 2023? |
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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: 0.7128571428571429 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8385714285714285 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
|
value: 0.8657142857142858 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
|
value: 0.9128571428571428 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7128571428571429 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27952380952380956 |
|
name: Cosine Precision@3 |
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- type: cosine_precision@5 |
|
value: 0.17314285714285713 |
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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: 0.7128571428571429 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8385714285714285 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8657142857142858 |
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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: 0.8160752408699454 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7850544217687072 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7883813094771759 |
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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 |
|
value: 0.7085714285714285 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8314285714285714 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8571428571428571 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.91 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7085714285714285 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.27714285714285714 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1714285714285714 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.091 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7085714285714285 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8314285714285714 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8571428571428571 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.91 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.810046642542136 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7782335600907029 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7817400926898996 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
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dataset: |
|
name: dim 256 |
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type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.7057142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8214285714285714 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8614285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8957142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7057142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2738095238095238 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17228571428571426 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08957142857142855 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7057142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8214285714285714 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8614285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8957142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.803237369609097 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7734654195011333 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7778038646628423 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
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type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6871428571428572 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8085714285714286 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8428571428571429 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8942857142857142 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6871428571428572 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2695238095238095 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16857142857142854 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08942857142857143 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6871428571428572 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8085714285714286 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8428571428571429 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8942857142857142 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7913904723614839 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7585782312925171 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.762610071156596 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.66 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7714285714285715 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8085714285714286 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8714285714285714 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.66 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2571428571428571 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1617142857142857 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08714285714285713 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.66 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7714285714285715 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8085714285714286 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8714285714285714 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7614379134484182 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7269172335600907 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7319569628864667 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base Financial Matryoshka |
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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. |
|
|
|
## 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 |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
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- json |
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- **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("ValentinaKim/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
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'Operating cash flow provides the primary source of cash to fund operating needs and capital expenditures.', |
|
'What is the primary source of cash used by the company to fund operating needs and capital expenditures?', |
|
'What kinds of products and services does the Company provide under the AARP Program?', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
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<!-- |
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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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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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|
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Information Retrieval |
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* Dataset: `dim_768` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.7129 | |
|
| cosine_accuracy@3 | 0.8386 | |
|
| cosine_accuracy@5 | 0.8657 | |
|
| cosine_accuracy@10 | 0.9129 | |
|
| cosine_precision@1 | 0.7129 | |
|
| cosine_precision@3 | 0.2795 | |
|
| cosine_precision@5 | 0.1731 | |
|
| cosine_precision@10 | 0.0913 | |
|
| cosine_recall@1 | 0.7129 | |
|
| cosine_recall@3 | 0.8386 | |
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| cosine_recall@5 | 0.8657 | |
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| cosine_recall@10 | 0.9129 | |
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| cosine_ndcg@10 | 0.8161 | |
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| cosine_mrr@10 | 0.7851 | |
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| **cosine_map@100** | **0.7884** | |
|
|
|
#### 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 | 0.7086 | |
|
| cosine_accuracy@3 | 0.8314 | |
|
| cosine_accuracy@5 | 0.8571 | |
|
| cosine_accuracy@10 | 0.91 | |
|
| cosine_precision@1 | 0.7086 | |
|
| cosine_precision@3 | 0.2771 | |
|
| cosine_precision@5 | 0.1714 | |
|
| cosine_precision@10 | 0.091 | |
|
| cosine_recall@1 | 0.7086 | |
|
| cosine_recall@3 | 0.8314 | |
|
| cosine_recall@5 | 0.8571 | |
|
| cosine_recall@10 | 0.91 | |
|
| cosine_ndcg@10 | 0.81 | |
|
| cosine_mrr@10 | 0.7782 | |
|
| **cosine_map@100** | **0.7817** | |
|
|
|
#### 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 | 0.7057 | |
|
| cosine_accuracy@3 | 0.8214 | |
|
| cosine_accuracy@5 | 0.8614 | |
|
| cosine_accuracy@10 | 0.8957 | |
|
| cosine_precision@1 | 0.7057 | |
|
| cosine_precision@3 | 0.2738 | |
|
| cosine_precision@5 | 0.1723 | |
|
| cosine_precision@10 | 0.0896 | |
|
| cosine_recall@1 | 0.7057 | |
|
| cosine_recall@3 | 0.8214 | |
|
| cosine_recall@5 | 0.8614 | |
|
| cosine_recall@10 | 0.8957 | |
|
| cosine_ndcg@10 | 0.8032 | |
|
| cosine_mrr@10 | 0.7735 | |
|
| **cosine_map@100** | **0.7778** | |
|
|
|
#### 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 | 0.6871 | |
|
| cosine_accuracy@3 | 0.8086 | |
|
| cosine_accuracy@5 | 0.8429 | |
|
| cosine_accuracy@10 | 0.8943 | |
|
| cosine_precision@1 | 0.6871 | |
|
| cosine_precision@3 | 0.2695 | |
|
| cosine_precision@5 | 0.1686 | |
|
| cosine_precision@10 | 0.0894 | |
|
| cosine_recall@1 | 0.6871 | |
|
| cosine_recall@3 | 0.8086 | |
|
| cosine_recall@5 | 0.8429 | |
|
| cosine_recall@10 | 0.8943 | |
|
| cosine_ndcg@10 | 0.7914 | |
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| cosine_mrr@10 | 0.7586 | |
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| **cosine_map@100** | **0.7626** | |
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#### Information Retrieval |
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* Dataset: `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:----------| |
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| cosine_accuracy@1 | 0.66 | |
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| cosine_accuracy@3 | 0.7714 | |
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| cosine_accuracy@5 | 0.8086 | |
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| cosine_accuracy@10 | 0.8714 | |
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| cosine_precision@1 | 0.66 | |
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| cosine_precision@3 | 0.2571 | |
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| cosine_precision@5 | 0.1617 | |
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| cosine_precision@10 | 0.0871 | |
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| cosine_recall@1 | 0.66 | |
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| cosine_recall@3 | 0.7714 | |
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| cosine_recall@5 | 0.8086 | |
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| cosine_recall@10 | 0.8714 | |
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| cosine_ndcg@10 | 0.7614 | |
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| cosine_mrr@10 | 0.7269 | |
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| **cosine_map@100** | **0.732** | |
|
|
|
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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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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|
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* Dataset: json |
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* Size: 6,300 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
|
| details | <ul><li>min: 6 tokens</li><li>mean: 45.81 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.26 tokens</li><li>max: 43 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| |
|
| <code>For the year ended December 31, 2023, Alphabet Inc. reported a net cash provided by operating activities of $101,746 million.</code> | <code>What was the net cash provided by operating activities for Alphabet Inc. in 2023?</code> | |
|
| <code>Our History In 2000, ICE was founded with the idea of transforming energy markets by creating a network that removed barriers and provided greater transparency, efficiency and access.</code> | <code>When was Intercontinental Exchange, Inc. founded, and what was its initial focus?</code> | |
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| <code>Item 8. Financial Statements and Supplementary Data The index to Financial Statements and Supplementary Data is presented</code> | <code>What is presented in Item 8 according to Financial Statements and Supplementary Data?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 16 |
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- `gradient_accumulation_steps`: 32 |
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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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- `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 |
|
<details><summary>Click to expand</summary> |
|
|
|
- `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 |
|
- `per_device_train_batch_size`: 16 |
|
- `per_device_eval_batch_size`: 8 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 32 |
|
- `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 |
|
- `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 |
|
- `no_cuda`: False |
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- `use_cpu`: False |
|
- `use_mps_device`: False |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
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- `tf32`: False |
|
- `local_rank`: 0 |
|
- `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 |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `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} |
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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} |
|
- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
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- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.9746 | 6 | - | 0.7258 | 0.7501 | 0.7513 | 0.6860 | 0.7589 | |
|
| 1.6244 | 10 | 1.4436 | - | - | - | - | - | |
|
| 1.9492 | 12 | - | 0.7494 | 0.7733 | 0.7800 | 0.7187 | 0.7827 | |
|
| 2.9239 | 18 | - | 0.7601 | 0.7796 | 0.7813 | 0.7312 | 0.7897 | |
|
| 3.2487 | 20 | 0.6159 | - | - | - | - | - | |
|
| **3.8985** | **24** | **-** | **0.7626** | **0.7778** | **0.7817** | **0.732** | **0.7884** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.14 |
|
- Sentence Transformers: 3.1.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.34.2 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
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## 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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|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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