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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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- dataset_size:1K<n<10K |
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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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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: Our effective tax rate for 2023 was 18%. |
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sentences: |
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- What was the effective tax rate in fiscal 2023? |
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- What are some key goals of the corporation related to climate change? |
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- In which item is Note 10, discussing Legal Proceedings, included? |
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- source_sentence: What kind of services does Equifax provide? |
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sentences: |
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- What is the primary business of Equifax Inc.? |
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- What new production locations and vehicle models were active in 2023? |
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- How much did AbbVie's gross margin percentage decrease in 2023 compared to 2022? |
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- source_sentence: What was the effective tax rate in 2023? |
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sentences: |
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- What was the effective tax rate for fiscal year 2023? |
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- How long do Enterprise Agreements last and who are they designed for? |
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- What was Ellen Copaken's professional role prior to joining AMC? |
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- source_sentence: What former roles has Indra K. Nooyi held? |
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sentences: |
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- Indra K. Nooyi | 68 | Former Chair and CEO, PepsiCo, Inc. |
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- What is the valuation allowance of the company as of January 31, 2023? |
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- What was the effective tax rate for fiscal 2023? |
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- source_sentence: The net earnings margin in 2023 was 6.0%. |
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sentences: |
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- What was the net earnings margin in 2023? |
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- What caused the slight decline in Workforce Solutions revenue in 2023? |
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- What does it mean when an item is 'incorporated by reference' in a document? |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: BGE base Financial Matryoshka |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 768 |
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type: dim_768 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.7257142857142858 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8514285714285714 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8828571428571429 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9142857142857143 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7257142857142858 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28380952380952373 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17657142857142857 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09142857142857141 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7257142857142858 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8514285714285714 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8828571428571429 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9142857142857143 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8232947560533131 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7937823129251699 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7965741135480359 |
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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: 0.7257142857142858 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8542857142857143 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8757142857142857 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.91 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.7257142857142858 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.28476190476190477 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17514285714285713 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09099999999999998 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7257142857142858 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8542857142857143 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8757142857142857 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.91 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8215329948771338 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7927670068027208 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7959270152786184 |
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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: 0.71 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.85 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8671428571428571 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9085714285714286 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.71 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2833333333333333 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1734285714285714 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09085714285714284 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.71 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.85 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8671428571428571 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9085714285714286 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8139428654682047 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7832817460317458 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7863373038655584 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 128 |
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type: dim_128 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.6814285714285714 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8157142857142857 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8585714285714285 |
|
name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8942857142857142 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6814285714285714 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
|
value: 0.2719047619047619 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1717142857142857 |
|
name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.08942857142857143 |
|
name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.6814285714285714 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8157142857142857 |
|
name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8585714285714285 |
|
name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8942857142857142 |
|
name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7914768113496716 |
|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7581626984126983 |
|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7616459239835561 |
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name: Cosine Map@100 |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: dim 64 |
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type: dim_64 |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.66 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.78 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8071428571428572 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.87 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.66 |
|
name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.26 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16142857142857142 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.087 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
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value: 0.66 |
|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.78 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8071428571428572 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.87 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.763736298979858 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7301014739229026 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7342830326633573 |
|
name: Cosine Map@100 |
|
--- |
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|
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# BGE base Financial Matryoshka |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
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## Model Details |
|
|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> |
|
- **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 |
|
|
|
### 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 |
|
|
|
``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Normalize() |
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) |
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``` |
|
|
|
## Usage |
|
|
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
|
```bash |
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pip install -U sentence-transformers |
|
``` |
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|
|
Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
|
# Download from the 🤗 Hub |
|
model = SentenceTransformer("MugheesAwan11/bge-base-financial-matryoshka") |
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# Run inference |
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sentences = [ |
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'The net earnings margin in 2023 was 6.0%.', |
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'What was the net earnings margin in 2023?', |
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'What caused the slight decline in Workforce Solutions revenue in 2023?', |
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] |
|
embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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|
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
|
print(similarities.shape) |
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# [3, 3] |
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``` |
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|
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<!-- |
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### Direct Usage (Transformers) |
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|
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<details><summary>Click to see the direct usage in Transformers</summary> |
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|
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</details> |
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--> |
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|
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<!-- |
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### Downstream Usage (Sentence Transformers) |
|
|
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You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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|
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</details> |
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--> |
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|
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<!-- |
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### Out-of-Scope Use |
|
|
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*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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* 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) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.7257 | |
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| cosine_accuracy@3 | 0.8514 | |
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| cosine_accuracy@5 | 0.8829 | |
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| cosine_accuracy@10 | 0.9143 | |
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| cosine_precision@1 | 0.7257 | |
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| cosine_precision@3 | 0.2838 | |
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| cosine_precision@5 | 0.1766 | |
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| cosine_precision@10 | 0.0914 | |
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| cosine_recall@1 | 0.7257 | |
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| cosine_recall@3 | 0.8514 | |
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| cosine_recall@5 | 0.8829 | |
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| cosine_recall@10 | 0.9143 | |
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| cosine_ndcg@10 | 0.8233 | |
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| cosine_mrr@10 | 0.7938 | |
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| **cosine_map@100** | **0.7966** | |
|
|
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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) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.7257 | |
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| cosine_accuracy@3 | 0.8543 | |
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| cosine_accuracy@5 | 0.8757 | |
|
| cosine_accuracy@10 | 0.91 | |
|
| cosine_precision@1 | 0.7257 | |
|
| cosine_precision@3 | 0.2848 | |
|
| cosine_precision@5 | 0.1751 | |
|
| cosine_precision@10 | 0.091 | |
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| cosine_recall@1 | 0.7257 | |
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| cosine_recall@3 | 0.8543 | |
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| cosine_recall@5 | 0.8757 | |
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| cosine_recall@10 | 0.91 | |
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| cosine_ndcg@10 | 0.8215 | |
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| cosine_mrr@10 | 0.7928 | |
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| **cosine_map@100** | **0.7959** | |
|
|
|
#### Information Retrieval |
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* Dataset: `dim_256` |
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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 | 0.71 | |
|
| cosine_accuracy@3 | 0.85 | |
|
| cosine_accuracy@5 | 0.8671 | |
|
| cosine_accuracy@10 | 0.9086 | |
|
| cosine_precision@1 | 0.71 | |
|
| cosine_precision@3 | 0.2833 | |
|
| cosine_precision@5 | 0.1734 | |
|
| cosine_precision@10 | 0.0909 | |
|
| cosine_recall@1 | 0.71 | |
|
| cosine_recall@3 | 0.85 | |
|
| cosine_recall@5 | 0.8671 | |
|
| cosine_recall@10 | 0.9086 | |
|
| cosine_ndcg@10 | 0.8139 | |
|
| cosine_mrr@10 | 0.7833 | |
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| **cosine_map@100** | **0.7863** | |
|
|
|
#### Information Retrieval |
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* Dataset: `dim_128` |
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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 | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.6814 | |
|
| cosine_accuracy@3 | 0.8157 | |
|
| cosine_accuracy@5 | 0.8586 | |
|
| cosine_accuracy@10 | 0.8943 | |
|
| cosine_precision@1 | 0.6814 | |
|
| cosine_precision@3 | 0.2719 | |
|
| cosine_precision@5 | 0.1717 | |
|
| cosine_precision@10 | 0.0894 | |
|
| cosine_recall@1 | 0.6814 | |
|
| cosine_recall@3 | 0.8157 | |
|
| cosine_recall@5 | 0.8586 | |
|
| cosine_recall@10 | 0.8943 | |
|
| cosine_ndcg@10 | 0.7915 | |
|
| cosine_mrr@10 | 0.7582 | |
|
| **cosine_map@100** | **0.7616** | |
|
|
|
#### Information Retrieval |
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* Dataset: `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.66 | |
|
| cosine_accuracy@3 | 0.78 | |
|
| cosine_accuracy@5 | 0.8071 | |
|
| cosine_accuracy@10 | 0.87 | |
|
| cosine_precision@1 | 0.66 | |
|
| cosine_precision@3 | 0.26 | |
|
| cosine_precision@5 | 0.1614 | |
|
| cosine_precision@10 | 0.087 | |
|
| cosine_recall@1 | 0.66 | |
|
| cosine_recall@3 | 0.78 | |
|
| cosine_recall@5 | 0.8071 | |
|
| cosine_recall@10 | 0.87 | |
|
| cosine_ndcg@10 | 0.7637 | |
|
| cosine_mrr@10 | 0.7301 | |
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| **cosine_map@100** | **0.7343** | |
|
|
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<!-- |
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## Bias, Risks and Limitations |
|
|
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 6,300 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 46.61 tokens</li><li>max: 289 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.58 tokens</li><li>max: 45 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| |
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| <code>Insurance Medical Membership at December 31, 2020 for Florida includes Individual Medicare Advantage (851.3 thousand), Group Medicare Advantage (9.1 thousand), Medicare stand-alone PDP (131.9 thousand), Medicare Supplement (17.5 thousand), State-based contracts and Other (656.6 thousand), Fully-insured commercial Group (73.8 thousand), ASO (24.5 thousand), totaling 1,764.7 thousand members.</code> | <code>How is Florida's total insurance medical membership detailed in the data for December 31, 2023?</code> | |
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| <code>For the year ended December 31, 2023, the total provision for income taxes was $836 million, which includes both current and deferred tax amounts.</code> | <code>What was the total provision for income taxes at the end of 2023?</code> | |
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| <code>Pursuant to the IRA, under Sections 48, 48E and 25D of the Internal Revenue Code (“IRC”), standalone energy storage technology is eligible for a tax credit between 6% and 50% of qualified expenditures, regardless of the source of energy, which may be claimed by our customers for storage systems they purchase or by us for arrangements where we own the systems.</code> | <code>Under what sections of the Internal Revenue Code can standalone energy storage technology receive a tax credit?</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `gradient_accumulation_steps`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 2 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: 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 |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 16 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 2 |
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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`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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|
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
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| 0.8122 | 10 | 1.4587 | - | - | - | - | - | |
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| 0.9746 | 12 | - | 0.7544 | 0.7722 | 0.7809 | 0.7118 | 0.7804 | |
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| 1.6244 | 20 | 0.6938 | - | - | - | - | - | |
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| **1.9492** | **24** | **-** | **0.7586** | **0.779** | **0.7876** | **0.7197** | **0.785** | |
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| 0.8122 | 10 | 0.5238 | - | - | - | - | - | |
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| 0.9746 | 12 | - | 0.7602 | 0.7815 | 0.7928 | 0.7285 | 0.7942 | |
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| 1.6244 | 20 | 0.4172 | - | - | - | - | - | |
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| **1.9492** | **24** | **-** | **0.7616** | **0.7863** | **0.7959** | **0.7343** | **0.7966** | |
|
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.14 |
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- Sentence Transformers: 3.0.0 |
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- Transformers: 4.41.2 |
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- PyTorch: 2.1.2+cu121 |
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- Accelerate: 0.30.1 |
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- Datasets: 2.19.1 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MatryoshkaLoss |
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```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
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year={2024}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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*Clearly define terms in order to be accessible across audiences.* |
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*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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