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--- |
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base_model: BAAI/bge-base-en-v1.5 |
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language: |
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- en |
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library_name: sentence-transformers |
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license: apache-2.0 |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6300 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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widget: |
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- source_sentence: There are no relevant matters to disclose under this Item for this |
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period. |
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sentences: |
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- How much did non-cash items contribute to the cash provided by operating activities |
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in fiscal 2023? |
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- Are there any legal matters under Item 3 that need to be disclosed for this period? |
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- What is the primary therapeutic use of Linzess (linaclotide)? |
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- source_sentence: As of December 31, 2023, we had a $500,000 revolving credit facility |
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with JPMorgan Chase Bank as administrative agent, with an interest rate based |
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on the SOFR plus 1.475%, a commitment fee of 0.175% for unused amounts, and conditions |
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such as maintaining a total leverage ratio of less than 3.0x and a consolidated |
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fixed charge coverage ratio of greater than 1.5x. |
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sentences: |
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- What percentage of U.S. admissions revenues in 2023 was attributed to films from |
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the company's seven largest movie studio distributors? |
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- What are the terms of the revolving credit facility agreement with JPMorgan as |
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of December 31, 2023? |
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- What was the postpaid churn rate for AT&T Inc. in 2023? |
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- source_sentence: Gross margin increased from $22,095 million in 2022 to $24,690 |
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million in 2023, amounting to a $2,595 million increase. |
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sentences: |
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- How much did the gross margin increase in fiscal year 2023 compared to 2022? |
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- What percentage of Meta's U.S. workforce in 2023 were represented by people with |
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disabilities, veterans, and members of the LGBTQ+ community? |
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- How many FedEx-branded packaging produced in 2022 was third-party certified? |
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- source_sentence: NHTSA has proposed CAFE standards for model years 2027–2031, and |
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the EPA has drafted GHG emission standards for 2027–2032. Both sets of standards |
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are awaiting finalization. |
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sentences: |
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- What methods does the company use to advertise its products? |
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- What types of products does Garmin design, develop, and distribute? |
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- What are the projected years covered by the new CAFE and GHG emission standards |
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proposed by NHTSA and the EPA? |
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- source_sentence: As of December 31, 2023, the fair value and amortized cost, net |
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of valuation allowance, for the Republic of Korea's government securities were |
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$1,784 million and $1,723 million respectively. |
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sentences: |
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- What was the fair value and amortized cost, net of valuation allowance, for the |
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Republic of Korea's government securities as of December 31, 2023? |
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- How does the company advance autonomous vehicle technology? |
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- What were the key factors affecting the company's cash flow from operations in |
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fiscal 2023? |
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model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.6871428571428572 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8285714285714286 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8571428571428571 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9071428571428571 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.6871428571428572 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27619047619047615 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1714285714285714 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
|
value: 0.0907142857142857 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.6871428571428572 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8285714285714286 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8571428571428571 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9071428571428571 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7981646895635455 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7633208616780044 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7670469746658456 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
|
value: 0.69 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8542857142857143 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9042857142857142 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.69 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2723809523809524 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17085714285714282 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09042857142857141 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.69 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8171428571428572 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8542857142857143 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9042857142857142 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7976622307973412 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7636388888888889 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7675482221709721 |
|
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 256 |
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type: dim_256 |
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metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6857142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8142857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8514285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8957142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6857142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2714285714285714 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17028571428571426 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08957142857142855 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6857142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8142857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8514285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8957142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7916274982255576 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7582437641723355 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7624248845655235 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
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dataset: |
|
name: dim 128 |
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type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6757142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8414285714285714 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8885714285714286 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6757142857142857 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26666666666666666 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16828571428571426 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08885714285714286 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6757142857142857 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8414285714285714 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8885714285714286 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.781962439522339 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7478424036281178 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7523517680786094 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6414285714285715 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7657142857142857 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7957142857142857 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8585714285714285 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6414285714285715 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2552380952380952 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.15914285714285714 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08585714285714285 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6414285714285715 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7657142857142857 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7957142857142857 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8585714285714285 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7479917583081255 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7129206349206347 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7185335911194088 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE base Financial Matryoshka |
|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
- **Model Type:** Sentence Transformer |
|
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
|
- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
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- json |
|
- **Language:** en |
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- **License:** apache-2.0 |
|
|
|
### Model Sources |
|
|
|
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
|
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
|
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
SentenceTransformer( |
|
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
|
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
|
(2): Normalize() |
|
) |
|
``` |
|
|
|
## Usage |
|
|
|
### Direct Usage (Sentence Transformers) |
|
|
|
First install the Sentence Transformers library: |
|
|
|
```bash |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
Then you can load this model and run inference. |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("Yuki20/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
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"As of December 31, 2023, the fair value and amortized cost, net of valuation allowance, for the Republic of Korea's government securities were $1,784 million and $1,723 million respectively.", |
|
"What was the fair value and amortized cost, net of valuation allowance, for the Republic of Korea's government securities as of December 31, 2023?", |
|
'How does the company advance autonomous vehicle technology?', |
|
] |
|
embeddings = model.encode(sentences) |
|
print(embeddings.shape) |
|
# [3, 768] |
|
|
|
# Get the similarity scores for the embeddings |
|
similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
|
# [3, 3] |
|
``` |
|
|
|
<!-- |
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### 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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</details> |
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--> |
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|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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|
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
|
### Metrics |
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|
|
#### Information Retrieval |
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* Dataset: `dim_768` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:----------| |
|
| cosine_accuracy@1 | 0.6871 | |
|
| cosine_accuracy@3 | 0.8286 | |
|
| cosine_accuracy@5 | 0.8571 | |
|
| cosine_accuracy@10 | 0.9071 | |
|
| cosine_precision@1 | 0.6871 | |
|
| cosine_precision@3 | 0.2762 | |
|
| cosine_precision@5 | 0.1714 | |
|
| cosine_precision@10 | 0.0907 | |
|
| cosine_recall@1 | 0.6871 | |
|
| cosine_recall@3 | 0.8286 | |
|
| cosine_recall@5 | 0.8571 | |
|
| cosine_recall@10 | 0.9071 | |
|
| cosine_ndcg@10 | 0.7982 | |
|
| cosine_mrr@10 | 0.7633 | |
|
| **cosine_map@100** | **0.767** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_512` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.69 | |
|
| cosine_accuracy@3 | 0.8171 | |
|
| cosine_accuracy@5 | 0.8543 | |
|
| cosine_accuracy@10 | 0.9043 | |
|
| cosine_precision@1 | 0.69 | |
|
| cosine_precision@3 | 0.2724 | |
|
| cosine_precision@5 | 0.1709 | |
|
| cosine_precision@10 | 0.0904 | |
|
| cosine_recall@1 | 0.69 | |
|
| cosine_recall@3 | 0.8171 | |
|
| cosine_recall@5 | 0.8543 | |
|
| cosine_recall@10 | 0.9043 | |
|
| cosine_ndcg@10 | 0.7977 | |
|
| cosine_mrr@10 | 0.7636 | |
|
| **cosine_map@100** | **0.7675** | |
|
|
|
#### 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 | 0.6857 | |
|
| cosine_accuracy@3 | 0.8143 | |
|
| cosine_accuracy@5 | 0.8514 | |
|
| cosine_accuracy@10 | 0.8957 | |
|
| cosine_precision@1 | 0.6857 | |
|
| cosine_precision@3 | 0.2714 | |
|
| cosine_precision@5 | 0.1703 | |
|
| cosine_precision@10 | 0.0896 | |
|
| cosine_recall@1 | 0.6857 | |
|
| cosine_recall@3 | 0.8143 | |
|
| cosine_recall@5 | 0.8514 | |
|
| cosine_recall@10 | 0.8957 | |
|
| cosine_ndcg@10 | 0.7916 | |
|
| cosine_mrr@10 | 0.7582 | |
|
| **cosine_map@100** | **0.7624** | |
|
|
|
#### 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 | 0.6757 | |
|
| cosine_accuracy@3 | 0.8 | |
|
| cosine_accuracy@5 | 0.8414 | |
|
| cosine_accuracy@10 | 0.8886 | |
|
| cosine_precision@1 | 0.6757 | |
|
| cosine_precision@3 | 0.2667 | |
|
| cosine_precision@5 | 0.1683 | |
|
| cosine_precision@10 | 0.0889 | |
|
| cosine_recall@1 | 0.6757 | |
|
| cosine_recall@3 | 0.8 | |
|
| cosine_recall@5 | 0.8414 | |
|
| cosine_recall@10 | 0.8886 | |
|
| cosine_ndcg@10 | 0.782 | |
|
| cosine_mrr@10 | 0.7478 | |
|
| **cosine_map@100** | **0.7524** | |
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#### Information Retrieval |
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* Dataset: `dim_64` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.6414 | |
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| cosine_accuracy@3 | 0.7657 | |
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| cosine_accuracy@5 | 0.7957 | |
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| cosine_accuracy@10 | 0.8586 | |
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| cosine_precision@1 | 0.6414 | |
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| cosine_precision@3 | 0.2552 | |
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| cosine_precision@5 | 0.1591 | |
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| cosine_precision@10 | 0.0859 | |
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| cosine_recall@1 | 0.6414 | |
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| cosine_recall@3 | 0.7657 | |
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| cosine_recall@5 | 0.7957 | |
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| cosine_recall@10 | 0.8586 | |
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| cosine_ndcg@10 | 0.748 | |
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| cosine_mrr@10 | 0.7129 | |
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| **cosine_map@100** | **0.7185** | |
|
|
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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|
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## Training Details |
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|
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### Training Dataset |
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|
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#### json |
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|
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* Dataset: json |
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* Size: 6,300 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 6 tokens</li><li>mean: 45.58 tokens</li><li>max: 289 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.34 tokens</li><li>max: 41 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| |
|
| <code>Billed business grew significantly over the past two years, increasing from $228.2 billion in 2021 to $281.6 billion in 2022, and reaching $329.5 billion in 2023.</code> | <code>How did billed business figures change from 2021 to 2023 as stated in the text?</code> | |
|
| <code>The Federal Reserve may limit an FHC’s ability to conduct permissible activities if it or any of its depository institution subsidiaries fails to maintain a well-capitalized and well-managed status. If non-compliant after 180 days, the Federal Reserve may require the FHC to divest its depository institution subsidiaries or cease all FHC Activities.</code> | <code>What happens if an FHC does not meet the Federal Reserve's eligibility requirements?</code> | |
|
| <code>For the fiscal year ending January 28, 2023, the basic net income per share was calculated to be $7.24, based on the net income and weighted average number of shares outstanding.</code> | <code>What was the basic net income per share in the fiscal year ending January 28, 2023?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `tf32`: False |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
|
|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
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- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
|
- `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 |
|
- `log_level_replica`: warning |
|
- `log_on_each_node`: True |
|
- `logging_nan_inf_filter`: True |
|
- `save_safetensors`: True |
|
- `save_on_each_node`: False |
|
- `save_only_model`: False |
|
- `restore_callback_states_from_checkpoint`: False |
|
- `no_cuda`: False |
|
- `use_cpu`: False |
|
- `use_mps_device`: False |
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- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: False |
|
- `fp16`: True |
|
- `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 |
|
- `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 |
|
- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
|
- `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 |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
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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 |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_768_cosine_map@100 | dim_512_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.8122 | 10 | 1.588 | - | - | - | - | - | |
|
| 0.9746 | 12 | - | 0.7593 | 0.7550 | 0.7472 | 0.7347 | 0.6970 | |
|
| 1.6244 | 20 | 0.7059 | - | - | - | - | - | |
|
| 1.9492 | 24 | - | 0.7623 | 0.7652 | 0.7559 | 0.7517 | 0.7127 | |
|
| 2.4365 | 30 | 0.4826 | - | - | - | - | - | |
|
| 2.9239 | 36 | - | 0.7675 | 0.7683 | 0.7603 | 0.7512 | 0.7166 | |
|
| 3.2487 | 40 | 0.3992 | - | - | - | - | - | |
|
| **3.8985** | **48** | **-** | **0.767** | **0.7675** | **0.7624** | **0.7524** | **0.7185** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.2.0 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.34.2 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
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## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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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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