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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: Consolidated Regulatory Capital - The capital requirements calculated |
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under the FRB’s Capital Framework include the capital conservation buffer requirements, |
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which are comprised of a 2.5% buffer (under the Advanced Capital Rules). |
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sentences: |
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- What was the effective income tax rate for the year ended December 31, 2023? |
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- What is the function of capital conservation buffer requirements in the FRB's |
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Capital Framework for banks like Group Inc. in 2023? |
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- What incentive does the Hawaiian Electric’s Battery Bonus grid services program |
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offer? |
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- source_sentence: Balance at beginning of year 2021 was $30 million and, after charge-offs, |
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recoveries, and provisions for credit losses, the balance at end of year was $18 |
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million. |
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sentences: |
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- Between what dates did CS&Co allegedly violate their duty to seek best execution |
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as per the plaintiffs' allegations in the lawsuit involving UBS Securities LLC? |
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- What were the balance at the beginning and the end of the year for credit loss |
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balances in 2021? |
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- How does the company handle leasehold improvements in terms of depreciation? |
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- source_sentence: The Compute reporting unit has an excess of fair value over carrying |
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value of 5% as of the annual test date. |
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sentences: |
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- What percent excess of fair value over carrying value did the Compute reporting |
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unit have as of the annual test date in 2023? |
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- What were the effective income tax rates for fiscal years 2023, 2022, and 2021, |
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and how did specific tax events affect these rates? |
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- When does the latest expiring European composition of matter patent (Supplementary |
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Protection Certificate) for STELARA expire? |
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- source_sentence: The net revenue decrease during 2023 in the Entertainment segment |
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was driven by lower entertainment productions and deliveries, reflecting the impact |
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of the several months-long strikes during 2023 by the Writers Guild of America |
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and the American actors' union, SAG-AFTRA. |
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sentences: |
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- What was the operating income for Google Cloud in 2023? |
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- How much did the company contribute to its pension and OPEB plans in 2023? |
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- What was the impact of the strikes by the Writers Guild of America and SAG-AFTRA |
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on the Entertainment segment's net revenues in 2023? |
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- source_sentence: As a REIT, future repatriation of incremental undistributed earnings |
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of the company's foreign subsidiaries will not be subject to federal or state |
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income tax, with the exception of foreign withholding taxes. |
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sentences: |
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- What tax implications apply to the future repatriation of incremental undistributed |
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earnings by a REIT from its foreign subsidiaries? |
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- What was the accrued liability for product recall related matters as of the end |
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of the fiscal year on June 30, 2023? |
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- What was the total amount of future interest payments associated with the Notes |
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as of September 30, 2023? |
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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 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7128571428571429 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8428571428571429 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.88 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.92 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7128571428571429 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28095238095238095 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.176 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09199999999999998 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7128571428571429 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8428571428571429 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.88 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.92 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8194470096208256 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7869285714285713 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7892168694112985 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 512 |
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type: dim_512 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7214285714285714 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8471428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8757142857142857 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
|
value: 0.9185714285714286 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7214285714285714 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2823809523809524 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17514285714285713 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
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value: 0.09185714285714286 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7214285714285714 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8471428571428572 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
|
value: 0.8757142857142857 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
|
value: 0.9185714285714286 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
|
value: 0.8222551376922121 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7912256235827663 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7935743687249276 |
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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: 0.7042857142857143 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8342857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8771428571428571 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9157142857142857 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7042857142857143 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.27809523809523806 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1754285714285714 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09157142857142857 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7042857142857143 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8342857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8771428571428571 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9157142857142857 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.813165438848782 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7800498866213152 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7822651539071127 |
|
name: Cosine Map@100 |
|
- task: |
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type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
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name: dim 128 |
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type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6971428571428572 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8142857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8557142857142858 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9028571428571428 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6971428571428572 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2714285714285714 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17114285714285712 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09028571428571427 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6971428571428572 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8142857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8557142857142858 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9028571428571428 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7996582219917312 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7667329931972787 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7700915959452638 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6742857142857143 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7942857142857143 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8257142857142857 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8742857142857143 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6742857142857143 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.26476190476190475 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16514285714285712 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08742857142857141 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6742857142857143 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7942857142857143 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8257142857142857 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8742857142857143 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7742733360934079 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7424053287981859 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7463231326238146 |
|
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 |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
|
- **Training Dataset:** |
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- json |
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- **Language:** en |
|
- **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("mezeidragos-lateral/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
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"As a REIT, future repatriation of incremental undistributed earnings of the company's foreign subsidiaries will not be subject to federal or state income tax, with the exception of foreign withholding taxes.", |
|
'What tax implications apply to the future repatriation of incremental undistributed earnings by a REIT from its foreign subsidiaries?', |
|
'What was the accrued liability for product recall related matters as of the end of the fiscal year on June 30, 2023?', |
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] |
|
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] |
|
``` |
|
|
|
<!-- |
|
### Direct Usage (Transformers) |
|
|
|
<details><summary>Click to see the direct usage in Transformers</summary> |
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|
|
</details> |
|
--> |
|
|
|
<!-- |
|
### Downstream Usage (Sentence Transformers) |
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|
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You can finetune this model on your own dataset. |
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|
|
<details><summary>Click to expand</summary> |
|
|
|
</details> |
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--> |
|
|
|
<!-- |
|
### Out-of-Scope Use |
|
|
|
*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 |
|
|
|
### Metrics |
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|
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#### Information Retrieval |
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|
|
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `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) |
|
|
|
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 | |
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|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------| |
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| cosine_accuracy@1 | 0.7129 | 0.7214 | 0.7043 | 0.6971 | 0.6743 | |
|
| cosine_accuracy@3 | 0.8429 | 0.8471 | 0.8343 | 0.8143 | 0.7943 | |
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| cosine_accuracy@5 | 0.88 | 0.8757 | 0.8771 | 0.8557 | 0.8257 | |
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| cosine_accuracy@10 | 0.92 | 0.9186 | 0.9157 | 0.9029 | 0.8743 | |
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| cosine_precision@1 | 0.7129 | 0.7214 | 0.7043 | 0.6971 | 0.6743 | |
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| cosine_precision@3 | 0.281 | 0.2824 | 0.2781 | 0.2714 | 0.2648 | |
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| cosine_precision@5 | 0.176 | 0.1751 | 0.1754 | 0.1711 | 0.1651 | |
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| cosine_precision@10 | 0.092 | 0.0919 | 0.0916 | 0.0903 | 0.0874 | |
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| cosine_recall@1 | 0.7129 | 0.7214 | 0.7043 | 0.6971 | 0.6743 | |
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| cosine_recall@3 | 0.8429 | 0.8471 | 0.8343 | 0.8143 | 0.7943 | |
|
| cosine_recall@5 | 0.88 | 0.8757 | 0.8771 | 0.8557 | 0.8257 | |
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| cosine_recall@10 | 0.92 | 0.9186 | 0.9157 | 0.9029 | 0.8743 | |
|
| **cosine_ndcg@10** | **0.8194** | **0.8223** | **0.8132** | **0.7997** | **0.7743** | |
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| cosine_mrr@10 | 0.7869 | 0.7912 | 0.78 | 0.7667 | 0.7424 | |
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| cosine_map@100 | 0.7892 | 0.7936 | 0.7823 | 0.7701 | 0.7463 | |
|
|
|
<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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|
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<!-- |
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### Recommendations |
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|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
|
|
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## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### json |
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|
|
* Dataset: json |
|
* Size: 6,300 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 4 tokens</li><li>mean: 44.62 tokens</li><li>max: 301 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 20.66 tokens</li><li>max: 45 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------| |
|
| <code>We provide transaction processing services (primarily authorization, clearing and settlement) to our financial institution and merchant clients through VisaNet, our proprietary advanced transaction processing network.</code> | <code>What are the primary transaction processing services provided by Visa through VisaNet?</code> | |
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| <code>Information about legal proceedings is included in Item 8 of the Annual Report on Form 10-K, as referenced in Item 3.</code> | <code>What item in the Annual Report on Form 10-K provides information about legal proceedings?</code> | |
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| <code>Investing activities used cash of $3.0 billion in 2022.</code> | <code>What was the net cash used by investing activities in 2022?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
|
1, |
|
1, |
|
1, |
|
1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `load_best_model_at_end`: True |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
|
|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `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`: None |
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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 |
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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`: None |
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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> |
|
|
|
### Training Logs |
|
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 | |
|
|:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.8122 | 10 | 1.5626 | - | - | - | - | - | |
|
| 1.0 | 13 | - | 0.8071 | 0.8040 | 0.7933 | 0.7781 | 0.7478 | |
|
| 1.5685 | 20 | 0.6111 | - | - | - | - | - | |
|
| 2.0 | 26 | - | 0.8173 | 0.8192 | 0.8111 | 0.7961 | 0.7661 | |
|
| 2.3249 | 30 | 0.4333 | - | - | - | - | - | |
|
| 3.0 | 39 | - | 0.8193 | 0.8211 | 0.8127 | 0.7996 | 0.7729 | |
|
| 3.0812 | 40 | 0.3465 | - | - | - | - | - | |
|
| **3.731** | **48** | **-** | **0.8194** | **0.8223** | **0.8132** | **0.7997** | **0.7743** | |
|
|
|
* The bold row denotes the saved checkpoint. |
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|
|
### Framework Versions |
|
- Python: 3.12.8 |
|
- Sentence Transformers: 3.3.1 |
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- Transformers: 4.48.0 |
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- PyTorch: 2.2.2 |
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- Accelerate: 1.2.1 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
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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", |
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month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
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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, |
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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}, |
|
year={2017}, |
|
eprint={1705.00652}, |
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archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
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