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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:6300 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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widget: |
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- source_sentence: Net cash used in financing activities in 2023 was $2,430 million. |
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sentences: |
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- What criteria does Airbnb, Inc. use to assess if an available-for-sale security |
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should be recorded as impaired on their financial statements? |
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- What was the total amount of net cash used in financing activities in 2023? |
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- How much did Visa authorize for its share repurchase program in October 2023? |
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- source_sentence: Microsoft® and Windows® are either registered trademarks or trademarks |
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of Microsoft Corporation in the United States and/or other countries. |
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sentences: |
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- Where does Eli Lilly and Company manufacture and distribute its products? |
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- What is the significance of Microsoft® and Windows® in relation to Microsoft Corporation? |
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- What percentage of total net revenue did the Americas region contribute in 2023? |
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- source_sentence: We make available free of charge on the Investor Relations section |
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of our corporate website all of the reports we file with or furnish to the SEC |
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as soon as reasonably practicable, after the reports are filed or furnished. |
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sentences: |
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- Is there a cost to access reports filed by Intuit Inc. with the SEC? |
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- What amount of cash, cash equivalents, and restricted cash did the company have |
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at the end of the period? |
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- Where in IBM’s 2023 Form 10-K can the Financial Statement Schedule be found? |
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- source_sentence: The U.S. Automobile Information and Disclosure Act also requires |
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manufacturers of motor vehicles to disclose certain information regarding the |
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manufacturer’s suggested retail price, optional equipment and pricing. |
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sentences: |
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- What does the Adjusted Effective Tax Rate measure exclude? |
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- What was the fair value of the total consideration transferred for the acquisition |
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discussed, and how was it composed? |
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- Which act requires U.S. automobile manufacturers to disclose certain pricing and |
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equipment information? |
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- source_sentence: Under the Insurance Act, Chubb's Bermuda domiciled subsidiaries |
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are prohibited from declaring or paying any dividends of more than 25 percent |
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of total statutory capital and surplus, as shown in its previous financial year |
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statutory balance sheet, unless at least seven days before payment of the dividends, |
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it files with the BMA an affidavit signed by at least two directors of the relevant |
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Bermuda domiciled subsidiary (one of whom must be a director resident in Bermuda) |
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and by the relevant Bermuda domiciled subsidiary’s principal representative, that |
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it will continue to meet its required solvency margins. Furthermore, Bermuda domiciled |
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subsidiaries may only declare and pay a dividend from retained earnings and a |
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dividend or distribution from contributed surplus if it has no reasonable grounds |
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for believing that it is, or would after the payment be, unable to pay its liabilities |
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as they become due, or if the realizable value of its assets would be less than |
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the aggregate of its liabilities. In addition, Chubb's Bermuda domiciled subsidiaries |
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must obtain the BMA's prior approval before reducing total statutory capital, |
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as shown in its previous financial year's financial statements, by 15 percent |
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or more. |
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sentences: |
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- What are the restrictions and requirements for Bermuda domiciled subsidiaries |
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regarding the distribution of dividends under the Insurance Act? |
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- What section deals with financial statements and supplementary data? |
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- What measures has the company implemented to ensure workplace safety? |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: BGE small 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 |
|
value: 0.7042857142857143 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8457142857142858 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.88 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
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value: 0.9242857142857143 |
|
name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7042857142857143 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28190476190476194 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.176 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
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value: 0.09242857142857142 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7042857142857143 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8457142857142858 |
|
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: 0.9242857142857143 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
|
value: 0.8153543862763872 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
|
value: 0.7803667800453513 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
|
value: 0.7829122109320609 |
|
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: 0.7057142857142857 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8471428571428572 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8685714285714285 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9242857142857143 |
|
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: 0.28238095238095234 |
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name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17371428571428568 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
|
value: 0.09242857142857142 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7057142857142857 |
|
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 |
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value: 0.8685714285714285 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9242857142857143 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.815124112835889 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
|
value: 0.7802040816326532 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7828080021041772 |
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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 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: 0.7071428571428572 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8385714285714285 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8757142857142857 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9228571428571428 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.7071428571428572 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.27952380952380956 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17514285714285713 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09228571428571428 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.7071428571428572 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8385714285714285 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8757142857142857 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9228571428571428 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.815223056195625 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7808248299319727 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7833488292208493 |
|
name: Cosine Map@100 |
|
--- |
|
|
|
# BGE small Financial Matryoshka |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
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|
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### Model Sources |
|
|
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- **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) |
|
|
|
### Full Model Architecture |
|
|
|
``` |
|
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}) |
|
(2): Normalize() |
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) |
|
``` |
|
|
|
## 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("haophancs/bge-base-financial-matryoshka") |
|
# Run inference |
|
sentences = [ |
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"Under the Insurance Act, Chubb's Bermuda domiciled subsidiaries are prohibited from declaring or paying any dividends of more than 25 percent of total statutory capital and surplus, as shown in its previous financial year statutory balance sheet, unless at least seven days before payment of the dividends, it files with the BMA an affidavit signed by at least two directors of the relevant Bermuda domiciled subsidiary (one of whom must be a director resident in Bermuda) and by the relevant Bermuda domiciled subsidiary’s principal representative, that it will continue to meet its required solvency margins. Furthermore, Bermuda domiciled subsidiaries may only declare and pay a dividend from retained earnings and a dividend or distribution from contributed surplus if it has no reasonable grounds for believing that it is, or would after the payment be, unable to pay its liabilities as they become due, or if the realizable value of its assets would be less than the aggregate of its liabilities. In addition, Chubb's Bermuda domiciled subsidiaries must obtain the BMA's prior approval before reducing total statutory capital, as shown in its previous financial year's financial statements, by 15 percent or more.", |
|
'What are the restrictions and requirements for Bermuda domiciled subsidiaries regarding the distribution of dividends under the Insurance Act?', |
|
'What section deals with financial statements and supplementary data?', |
|
] |
|
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) |
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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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### Downstream Usage (Sentence Transformers) |
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|
|
You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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|
|
</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
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|
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Information Retrieval |
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* Dataset: `dim_768` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.7043 | |
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| cosine_accuracy@3 | 0.8457 | |
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| cosine_accuracy@5 | 0.88 | |
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| cosine_accuracy@10 | 0.9243 | |
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| cosine_precision@1 | 0.7043 | |
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| cosine_precision@3 | 0.2819 | |
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| cosine_precision@5 | 0.176 | |
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| cosine_precision@10 | 0.0924 | |
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| cosine_recall@1 | 0.7043 | |
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| cosine_recall@3 | 0.8457 | |
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| cosine_recall@5 | 0.88 | |
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| cosine_recall@10 | 0.9243 | |
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| cosine_ndcg@10 | 0.8154 | |
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| cosine_mrr@10 | 0.7804 | |
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| **cosine_map@100** | **0.7829** | |
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|
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#### Information Retrieval |
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* Dataset: `dim_512` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.7057 | |
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| cosine_accuracy@3 | 0.8471 | |
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| cosine_accuracy@5 | 0.8686 | |
|
| cosine_accuracy@10 | 0.9243 | |
|
| cosine_precision@1 | 0.7057 | |
|
| cosine_precision@3 | 0.2824 | |
|
| cosine_precision@5 | 0.1737 | |
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| cosine_precision@10 | 0.0924 | |
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| cosine_recall@1 | 0.7057 | |
|
| cosine_recall@3 | 0.8471 | |
|
| cosine_recall@5 | 0.8686 | |
|
| cosine_recall@10 | 0.9243 | |
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| cosine_ndcg@10 | 0.8151 | |
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| cosine_mrr@10 | 0.7802 | |
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| **cosine_map@100** | **0.7828** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_384` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.7071 | |
|
| cosine_accuracy@3 | 0.8386 | |
|
| cosine_accuracy@5 | 0.8757 | |
|
| cosine_accuracy@10 | 0.9229 | |
|
| cosine_precision@1 | 0.7071 | |
|
| cosine_precision@3 | 0.2795 | |
|
| cosine_precision@5 | 0.1751 | |
|
| cosine_precision@10 | 0.0923 | |
|
| cosine_recall@1 | 0.7071 | |
|
| cosine_recall@3 | 0.8386 | |
|
| cosine_recall@5 | 0.8757 | |
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| cosine_recall@10 | 0.9229 | |
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| cosine_ndcg@10 | 0.8152 | |
|
| cosine_mrr@10 | 0.7808 | |
|
| **cosine_map@100** | **0.7833** | |
|
|
|
<!-- |
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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 |
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
--> |
|
|
|
## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 6,300 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 8 tokens</li><li>mean: 45.4 tokens</li><li>max: 252 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 20.43 tokens</li><li>max: 45 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------| |
|
| <code>In 2023, $2.2 billion or 5% was primarily related to patient co-pay assistance, cash discounts for prompt payment, distributor fees, and sales return provisions.</code> | <code>What was the amount of sales return provisions in 2023 as part of gross-to-net deductions?</code> | |
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| <code>Cash and cash equivalents were $21.9 billion at the end of 2023 as compared to $14.1 billion at the end of 2022, showing a $7.8 billion increase.</code> | <code>How much did cash and cash equivalents increase by the end of 2023 compared to the end of 2022?</code> | |
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| <code>The net increase in cash and cash equivalents for UnitedHealthcare in 2023 compared to 2022 was $72 million.</code> | <code>What was the net increase in cash and cash equivalents for UnitedHealthcare in 2023 compared to 2022?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
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"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
768, |
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512, |
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384 |
|
], |
|
"matryoshka_weights": [ |
|
1, |
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1, |
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1 |
|
], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
|
- `num_train_epochs`: 4 |
|
- `lr_scheduler_type`: cosine |
|
- `warmup_ratio`: 0.1 |
|
- `bf16`: True |
|
- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 16 |
|
- `eval_accumulation_steps`: None |
|
- `learning_rate`: 2e-05 |
|
- `weight_decay`: 0.0 |
|
- `adam_beta1`: 0.9 |
|
- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 4 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `lr_scheduler_kwargs`: {} |
|
- `warmup_ratio`: 0.1 |
|
- `warmup_steps`: 0 |
|
- `log_level`: passive |
|
- `log_level_replica`: warning |
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- `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 |
|
- `seed`: 42 |
|
- `data_seed`: None |
|
- `jit_mode_eval`: False |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: True |
|
- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `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 |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `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_384_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:| |
|
| 0.8122 | 10 | 0.8256 | - | - | - | |
|
| 0.9746 | 12 | - | 0.7719 | 0.7679 | 0.7652 | |
|
| 1.6244 | 20 | 0.2984 | - | - | - | |
|
| 1.9492 | 24 | - | 0.7784 | 0.7810 | 0.7791 | |
|
| 2.4365 | 30 | 0.201 | - | - | - | |
|
| 2.9239 | 36 | - | 0.7835 | 0.7832 | 0.7828 | |
|
| 3.2487 | 40 | 0.1705 | - | - | - | |
|
| **3.8985** | **48** | **-** | **0.7833** | **0.7828** | **0.7829** | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.12.2 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.2.0+cu121 |
|
- Accelerate: 0.31.0 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## 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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