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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6300 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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widget: |
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- source_sentence: Capital expenditures, which primarily reflected investments in |
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technical infrastructure, were $32.3 billion for the year ended December 31, 2023. |
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sentences: |
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- Where can you find the consolidated financial statements in the Annual Report |
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on Form 10-K? |
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- What were the total capital expenditures for Alphabet Inc. in 2023? |
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- How did Chevron's development strategy in the Permian Basin contribute to its |
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productivity? |
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- source_sentence: You can identify forward-looking statements by the use of forward-looking |
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terminology including “believes,” “expects,” “may,” “will,” “should,” “seeks,” |
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“intends,” “plans,” “pro forma,” “estimates,” “anticipates,” or the negative of |
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these words and phrases, other variations of these words and phrases or comparable |
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terminology. |
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sentences: |
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- What does the forward-looking terminology in financial documents imply? |
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- What seasons have higher domestic advertising revenue and what influences these |
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patterns? |
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- What is the role of Bank of America Corporation's management in relation to internal |
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control over financial reporting? |
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- source_sentence: For the year ended December 31, 2023, we recorded $3.6 million |
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of foreign currency transaction losses. |
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sentences: |
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- What was the total foreign currency transaction loss recorded for the year ended |
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December 31, 2023? |
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- What credit ratings were assigned to the company by Standard & Poor’s and Moody’s |
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at the end of 2022? |
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- What are The Home Depot's strategies for increasing diversity, equity, and inclusion? |
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- source_sentence: Gross margin contraction of 310 basis points primarily due to higher |
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product costs, reflecting higher input costs and inbound freight and logistics |
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costs and product mix, lower margins in NIKE Direct due to higher promotional |
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activity and a lower mix of full-price sales. |
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sentences: |
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- What fiduciary duties might a company have under ERISA? |
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- What were the significant contributors to the gross margin contraction and by |
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how many basis points did it contract? |
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- What typical reimbursement methods are used in the company's contracts with hospitals |
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for inpatient and outpatient services? |
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- source_sentence: As of December 31, 2023, we employed about 113,200 full-time persons |
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of whom approximately 62,400 were located outside the United States. In the United |
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States, we employed approximately 50,800 full-time persons. |
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sentences: |
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- What types of categories did eBay focus on in 2023, and how did they contribute |
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to the company's gross merchandise volume? |
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- What challenges do solar power system owners face with traditional string inverters? |
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- How many full-time employees were employed by the company as of December 31, 2023, |
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and how are they distributed geographically? |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 384 |
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type: dim_384 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7157142857142857 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8485714285714285 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8742857142857143 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9171428571428571 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7157142857142857 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28285714285714286 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17485714285714282 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09171428571428569 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7157142857142857 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8485714285714285 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8742857142857143 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9171428571428571 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8198819637056249 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7885175736961447 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7918328646013278 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 256 |
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type: dim_256 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7157142857142857 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8485714285714285 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8785714285714286 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9142857142857143 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7157142857142857 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28285714285714286 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17571428571428568 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09142857142857141 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7157142857142857 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8485714285714285 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8785714285714286 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9142857142857143 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8187635355625659 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7878270975056689 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7911673353002208 |
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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: 0.7057142857142857 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8371428571428572 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8642857142857143 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9085714285714286 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7057142857142857 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27904761904761904 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17285714285714285 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09085714285714284 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7057142857142857 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8371428571428572 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8642857142857143 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9085714285714286 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8090255333396114 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.777143424036281 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7807082191352167 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 64 |
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type: dim_64 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.6728571428571428 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8128571428571428 |
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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 |
|
|
- type: cosine_accuracy@10 |
|
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value: 0.8814285714285715 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
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value: 0.6728571428571428 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
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value: 0.27095238095238094 |
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|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
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value: 0.16999999999999998 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.08814285714285712 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
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value: 0.6728571428571428 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
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value: 0.8128571428571428 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
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value: 0.85 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
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value: 0.8814285714285715 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.782934506961568 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
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value: 0.7507721088435368 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
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value: 0.7551335288460688 |
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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 32 |
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type: dim_32 |
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metrics: |
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- type: cosine_accuracy@1 |
|
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value: 0.5957142857142858 |
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name: Cosine Accuracy@1 |
|
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- type: cosine_accuracy@3 |
|
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value: 0.7414285714285714 |
|
|
name: Cosine Accuracy@3 |
|
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- type: cosine_accuracy@5 |
|
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value: 0.7828571428571428 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
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value: 0.8314285714285714 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
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value: 0.5957142857142858 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
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value: 0.2471428571428571 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.15657142857142856 |
|
|
name: Cosine Precision@5 |
|
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- type: cosine_precision@10 |
|
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value: 0.08314285714285713 |
|
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name: Cosine Precision@10 |
|
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- type: cosine_recall@1 |
|
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value: 0.5957142857142858 |
|
|
name: Cosine Recall@1 |
|
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- type: cosine_recall@3 |
|
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value: 0.7414285714285714 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
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value: 0.7828571428571428 |
|
|
name: Cosine Recall@5 |
|
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- type: cosine_recall@10 |
|
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value: 0.8314285714285714 |
|
|
name: Cosine Recall@10 |
|
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- type: cosine_ndcg@10 |
|
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value: 0.7158751864189645 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
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value: 0.6787687074829931 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
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value: 0.6839925227099907 |
|
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name: Cosine Map@100 |
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--- |
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# BGE base Financial Matryoshka |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- json |
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- **Language:** en |
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- **License:** apache-2.0 |
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### Model Sources |
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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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|
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### Full Model Architecture |
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|
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("moresearch/bge-base-financial-matryoshka") |
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# Run inference |
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sentences = [ |
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'As of December 31, 2023, we employed about 113,200 full-time persons of whom approximately 62,400 were located outside the United States. In the United States, we employed approximately 50,800 full-time persons.', |
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'How many full-time employees were employed by the company as of December 31, 2023, and how are they distributed geographically?', |
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'What challenges do solar power system owners face with traditional string inverters?', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Datasets: `dim_384`, `dim_256`, `dim_128`, `dim_64` and `dim_32` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | dim_384 | dim_256 | dim_128 | dim_64 | dim_32 | |
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|:--------------------|:-----------|:-----------|:----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.7157 | 0.7157 | 0.7057 | 0.6729 | 0.5957 | |
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| cosine_accuracy@3 | 0.8486 | 0.8486 | 0.8371 | 0.8129 | 0.7414 | |
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| cosine_accuracy@5 | 0.8743 | 0.8786 | 0.8643 | 0.85 | 0.7829 | |
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| cosine_accuracy@10 | 0.9171 | 0.9143 | 0.9086 | 0.8814 | 0.8314 | |
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| cosine_precision@1 | 0.7157 | 0.7157 | 0.7057 | 0.6729 | 0.5957 | |
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| cosine_precision@3 | 0.2829 | 0.2829 | 0.279 | 0.271 | 0.2471 | |
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| cosine_precision@5 | 0.1749 | 0.1757 | 0.1729 | 0.17 | 0.1566 | |
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| cosine_precision@10 | 0.0917 | 0.0914 | 0.0909 | 0.0881 | 0.0831 | |
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| cosine_recall@1 | 0.7157 | 0.7157 | 0.7057 | 0.6729 | 0.5957 | |
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| cosine_recall@3 | 0.8486 | 0.8486 | 0.8371 | 0.8129 | 0.7414 | |
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| cosine_recall@5 | 0.8743 | 0.8786 | 0.8643 | 0.85 | 0.7829 | |
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| cosine_recall@10 | 0.9171 | 0.9143 | 0.9086 | 0.8814 | 0.8314 | |
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| **cosine_ndcg@10** | **0.8199** | **0.8188** | **0.809** | **0.7829** | **0.7159** | |
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| cosine_mrr@10 | 0.7885 | 0.7878 | 0.7771 | 0.7508 | 0.6788 | |
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| cosine_map@100 | 0.7918 | 0.7912 | 0.7807 | 0.7551 | 0.684 | |
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## Bias, Risks and Limitations |
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### Recommendations |
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## Training Details |
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### Training Dataset |
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#### json |
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* Dataset: json |
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* Size: 6,300 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | positive | anchor | |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 45.87 tokens</li><li>max: 288 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.32 tokens</li><li>max: 41 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>The company maintains a revolving credit facility that, unless extended, terminates on July 6, 2026.</code> | <code>What is the expiration date of the company's revolving credit facility, unless extended?</code> | |
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| <code>The management of Bank of America Corporation is responsible for establishing and maintaining adequate internal control over financial reporting. The Corporation’s internal control over financial reporting is designed to provide reasonable assurance about the reliability of financial reporting and the preparation of financial statements in accordance with accounting principles generally accepted in the United States of America. Management's responsibilities include maintaining records that, in reasonable detail, accurately and fairly reflect the transactions and dispositions of the assets of the Corporation; ensuring that transactions are recorded as necessary for the preparation of financial statements; and preventing or detecting unauthorized acquisition, use, or disposition of the Corporation’s assets that could have a material effect on the financial statements.</code> | <code>What is the role of Bank of America Corporation's management in relation to internal control over financial reporting?</code> | |
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| <code>In 2020, Gilead implemented multiple programs to train managers on inclusion and diversity topics and created strategies and initiatives focused on attracting, developing and retaining diverse talent and driving an inclusive culture in our workplace.</code> | <code>What initiatives has Gilead undertaken to promote workplace diversity?</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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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: 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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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | dim_384_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | dim_32_cosine_ndcg@10 | |
|
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|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:---------------------:| |
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| 0.8122 | 10 | 1.5733 | - | - | - | - | - | |
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| 0.9746 | 12 | - | 0.8075 | 0.8045 | 0.7876 | 0.7643 | 0.6844 | |
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| 1.6244 | 20 | 0.6549 | - | - | - | - | - | |
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| 1.9492 | 24 | - | 0.8188 | 0.8169 | 0.8035 | 0.7789 | 0.7107 | |
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| 2.4365 | 30 | 0.4373 | - | - | - | - | - | |
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| 2.9239 | 36 | - | 0.8210 | 0.8183 | 0.8079 | 0.7835 | 0.7161 | |
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| 3.2487 | 40 | 0.3951 | - | - | - | - | - | |
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| **3.8985** | **48** | **-** | **0.8199** | **0.8188** | **0.809** | **0.7829** | **0.7159** | |
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* 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: 3.3.1 |
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- Transformers: 4.46.3 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.1.1 |
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- Datasets: 3.1.0 |
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- Tokenizers: 0.20.3 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MatryoshkaLoss |
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```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
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year={2024}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
|
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
|
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``` |
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*Clearly define terms in order to be accessible across audiences.* |
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