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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: From 2021 to 2022, the operating revenue increased by 4%, from |
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$4,923.9 million to $5,122.2 million. |
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sentences: |
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- How much does the AMC Stubs A-List membership cost per month depending on the |
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geographic market? |
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- What was the percentage change in operating revenue from 2021 to 2022? |
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- What types of coverage does political risk insurance provide for commercial lenders? |
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- source_sentence: Our two operating segments are "Compute & Networking" and "Graphics." |
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Refer to Note 17 of the Notes to the Consolidated Financial Statements in Part |
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IV, Item 15 of this Annual Report on Form 10-K for additional information. |
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sentences: |
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- What was the noncash impairment charge recorded in the fourth quarter of 2023 |
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for the goodwill attributable to FedEx Dataworks? |
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- What are the two operating segments of NVIDIA as mentioned in the text? |
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- What is the disclosure threshold for environmental proceedings involving monetary |
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sanctions according to SEC regulations? |
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- source_sentence: For 2023, the weighted-average actuarial assumptions for retirement |
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plans included a service cost discount rate of 4.85% and a rate of increase in |
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compensation levels of 3.71%. |
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sentences: |
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- What are the actuarial assumptions for retirement plans discount rate and rate |
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of increase in compensation levels in 2023? |
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- Where are accrued interest and penalties related to unrecognized tax benefits |
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recorded? |
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- What is the purpose of the Employee Resource Groups (ERGs) in the organization? |
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- source_sentence: The Company is currently party to certain legal proceedings, none |
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of which we believe to be material to our business or financial condition. |
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sentences: |
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- What measures is The Hershey Company taking to ensure sufficient liquidity during |
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economic downturns? |
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- What is the impact of structural changes on the unit case volume and concentrate |
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sales volume of the company on a consolidated basis or at the geographic operating |
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segment level? |
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- What is the company's perspective on the impact of the legal proceedings on its |
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financial condition? |
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- source_sentence: We recognize gains and losses on pension and postretirement plan |
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assets and obligations immediately in Other income (expense) - net in our consolidated |
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statements of income. |
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sentences: |
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- Where are gains and losses on pension and postretirement plan assets and obligations |
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recognized in financial statements? |
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- What is the total amount of property, plant, and equipment, net, reported by the |
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company for the fiscal year 2023? |
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- What were the accumulated benefit obligation and fair value of plan assets for |
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certain U.S. pension plans with obligations exceeding assets as of December 31, |
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2023? |
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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: 0.6828571428571428 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8228571428571428 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.86 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9057142857142857 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.6828571428571428 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2742857142857143 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.172 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09057142857142855 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.6828571428571428 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8228571428571428 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.86 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9057142857142857 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7960843632092954 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7607987528344665 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7647429753660495 |
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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: 0.6842857142857143 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8228571428571428 |
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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: 0.9014285714285715 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.6842857142857143 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2742857142857143 |
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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: 0.09014285714285714 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.6842857142857143 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8228571428571428 |
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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: 0.9014285714285715 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7939749538465997 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7593849206349204 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7635559033333911 |
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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: 0.68 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8114285714285714 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.85 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8942857142857142 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.68 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2704761904761905 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.16999999999999998 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.08942857142857143 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.68 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8114285714285714 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.85 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8942857142857142 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7888779795440546 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
|
value: 0.7549767573696146 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7594249239569217 |
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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 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.6571428571428571 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.7942857142857143 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8342857142857143 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8885714285714286 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6571428571428571 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
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value: 0.26476190476190475 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16685714285714284 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08885714285714284 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
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value: 0.6571428571428571 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
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value: 0.7942857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
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value: 0.8342857142857143 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8885714285714286 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7729724847261471 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7360578231292516 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
|
value: 0.740309728715939 |
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name: Cosine Map@100 |
|
- 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: 0.6185714285714285 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
|
value: 0.76 |
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name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8657142857142858 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6185714285714285 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2533333333333333 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.15999999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08657142857142855 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6185714285714285 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.76 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8657142857142858 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7409253495656911 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7012964852607709 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7061843304820828 |
|
name: Cosine Map@100 |
|
--- |
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|
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# BGE base Financial Matryoshka |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5). 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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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
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|
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### Model Sources |
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|
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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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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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|
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## Usage |
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|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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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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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("anikulkar/bge-base-financial-matryoshka") |
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# Run inference |
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sentences = [ |
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'We recognize gains and losses on pension and postretirement plan assets and obligations immediately in Other income (expense) - net in our consolidated statements of income.', |
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'Where are gains and losses on pension and postretirement plan assets and obligations recognized in financial statements?', |
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'What is the total amount of property, plant, and equipment, net, reported by the company for the fiscal year 2023?', |
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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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|
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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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<!-- |
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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|
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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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|
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You can finetune this model on your own dataset. |
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|
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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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<!-- |
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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 | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.6829 | |
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| cosine_accuracy@3 | 0.8229 | |
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| cosine_accuracy@5 | 0.86 | |
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| cosine_accuracy@10 | 0.9057 | |
|
| cosine_precision@1 | 0.6829 | |
|
| cosine_precision@3 | 0.2743 | |
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| cosine_precision@5 | 0.172 | |
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| cosine_precision@10 | 0.0906 | |
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| cosine_recall@1 | 0.6829 | |
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| cosine_recall@3 | 0.8229 | |
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| cosine_recall@5 | 0.86 | |
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| cosine_recall@10 | 0.9057 | |
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| cosine_ndcg@10 | 0.7961 | |
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| cosine_mrr@10 | 0.7608 | |
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| **cosine_map@100** | **0.7647** | |
|
|
|
#### Information Retrieval |
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* Dataset: `dim_512` |
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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 | |
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|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.6843 | |
|
| cosine_accuracy@3 | 0.8229 | |
|
| cosine_accuracy@5 | 0.8557 | |
|
| cosine_accuracy@10 | 0.9014 | |
|
| cosine_precision@1 | 0.6843 | |
|
| cosine_precision@3 | 0.2743 | |
|
| cosine_precision@5 | 0.1711 | |
|
| cosine_precision@10 | 0.0901 | |
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| cosine_recall@1 | 0.6843 | |
|
| cosine_recall@3 | 0.8229 | |
|
| cosine_recall@5 | 0.8557 | |
|
| cosine_recall@10 | 0.9014 | |
|
| cosine_ndcg@10 | 0.794 | |
|
| cosine_mrr@10 | 0.7594 | |
|
| **cosine_map@100** | **0.7636** | |
|
|
|
#### 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 | 0.68 | |
|
| cosine_accuracy@3 | 0.8114 | |
|
| cosine_accuracy@5 | 0.85 | |
|
| cosine_accuracy@10 | 0.8943 | |
|
| cosine_precision@1 | 0.68 | |
|
| cosine_precision@3 | 0.2705 | |
|
| cosine_precision@5 | 0.17 | |
|
| cosine_precision@10 | 0.0894 | |
|
| cosine_recall@1 | 0.68 | |
|
| cosine_recall@3 | 0.8114 | |
|
| cosine_recall@5 | 0.85 | |
|
| cosine_recall@10 | 0.8943 | |
|
| cosine_ndcg@10 | 0.7889 | |
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| cosine_mrr@10 | 0.755 | |
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| **cosine_map@100** | **0.7594** | |
|
|
|
#### 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 | 0.6571 | |
|
| cosine_accuracy@3 | 0.7943 | |
|
| cosine_accuracy@5 | 0.8343 | |
|
| cosine_accuracy@10 | 0.8886 | |
|
| cosine_precision@1 | 0.6571 | |
|
| cosine_precision@3 | 0.2648 | |
|
| cosine_precision@5 | 0.1669 | |
|
| cosine_precision@10 | 0.0889 | |
|
| cosine_recall@1 | 0.6571 | |
|
| cosine_recall@3 | 0.7943 | |
|
| cosine_recall@5 | 0.8343 | |
|
| cosine_recall@10 | 0.8886 | |
|
| cosine_ndcg@10 | 0.773 | |
|
| cosine_mrr@10 | 0.7361 | |
|
| **cosine_map@100** | **0.7403** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_64` |
|
* 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 | 0.6186 | |
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| cosine_accuracy@3 | 0.76 | |
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| cosine_accuracy@5 | 0.8 | |
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| cosine_accuracy@10 | 0.8657 | |
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| cosine_precision@1 | 0.6186 | |
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| cosine_precision@3 | 0.2533 | |
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| cosine_precision@5 | 0.16 | |
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| cosine_precision@10 | 0.0866 | |
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| cosine_recall@1 | 0.6186 | |
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| cosine_recall@3 | 0.76 | |
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| cosine_recall@5 | 0.8 | |
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| cosine_recall@10 | 0.8657 | |
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| cosine_ndcg@10 | 0.7409 | |
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| cosine_mrr@10 | 0.7013 | |
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| **cosine_map@100** | **0.7062** | |
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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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### Recommendations |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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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: 8 tokens</li><li>mean: 45.24 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.71 tokens</li><li>max: 45 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Changes in Costs. Our costs are subject to fluctuations, particularly due to changes in commodity and input material prices, transportation costs, other broader inflationary impacts and our own productivity efforts. We have significant exposures to certain commodities and input materials, in particular certain oil-derived materials like resins and paper-based materials like pulp. Volatility in the market price of these commodities and input materials has a direct impact on our costs. Disruptions in our manufacturing, supply and distribution operations due to energy shortages, natural disasters, labor or freight constraints have impacted our costs and could do so in the future. New or increased legal or regulatory requirements, along with initiatives to meet our sustainability goals, could also result in increased costs due to higher material costs and investments in facilities and equipment. We strive to implement, achieve and sustain cost improvement plans, including supply chain optimization and general overhead and workforce optimization. Increased pricing in response to certain inflationary or cost increases may also offset portions of the cost impacts; however, such price increases may impact product consumption. If we are unable to manage cost impacts through pricing actions and consistent productivity improvements, it may adversely impact our net sales, gross margin, operating margin, net earnings and cash flows.</code> | <code>How did Procter & Gamble manage the fluctuations in costs, particularly related to commodities and input materials?</code> | |
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| <code>As of October 1, 2023 we had ¥5 billion, or $33.5 million, of borrowings outstanding under these credit facilities.</code> | <code>How much was borrowed under the Japanese yen-denominated credit facilities as of October 1, 2023?</code> | |
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| <code>AutoZone sells automotive hard parts, maintenance items, accessories and non-automotive products through www.autozone.com, and commercial customers can make purchases through www.autozonepro.com. Additionally, the ALLDATA brand of automotive diagnostic, repair, collision and shop management software is sold through www.alldata.com.</code> | <code>What online platforms does AutoZone use for selling automotive products and services?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: 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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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: 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 |
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|
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</details> |
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|
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### Training Logs |
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| 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 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.8122 | 10 | 1.5647 | - | - | - | - | - | |
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| 0.9746 | 12 | - | 0.7160 | 0.7404 | 0.7515 | 0.6797 | 0.7533 | |
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| 1.6244 | 20 | 0.6629 | - | - | - | - | - | |
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| 1.9492 | 24 | - | 0.7340 | 0.7582 | 0.7611 | 0.6996 | 0.7603 | |
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| 2.4365 | 30 | 0.4811 | - | - | - | - | - | |
|
| **2.9239** | **36** | **-** | **0.7403** | **0.759** | **0.7638** | **0.7056** | **0.7646** | |
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| 3.2487 | 40 | 0.4046 | - | - | - | - | - | |
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| 3.8985 | 48 | - | 0.7403 | 0.7594 | 0.7636 | 0.7062 | 0.7647 | |
|
|
|
* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.3.0+cu121 |
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- Accelerate: 0.31.0 |
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- Datasets: 2.19.2 |
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- Tokenizers: 0.19.1 |
|
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@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}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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|
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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*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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