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--- |
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language: |
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- en |
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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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- dense |
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- generated_from_trainer |
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- dataset_size:2699 |
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- loss:CachedMultipleNegativesRankingLoss |
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base_model: google/embeddinggemma-300m |
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widget: |
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- source_sentence: For a conventional conforming loan, what are the common down payment |
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amounts? |
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sentences: |
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- fannie_mae_selling_guide_chunk_001 |
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- fannie_mae_selling_guide |
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- Standard down payment options for a conventional conforming loan range from 3 |
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to 20 of the purchase price. |
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- source_sentence: How is the cash required for mortgage reserves confirmed by a lender? |
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sentences: |
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- freddie_mac_guide |
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- Lenders verify assets by requiring two months of consecutive statements for all |
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checking, savings, and investment accounts. |
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- freddie_mac_guide_chunk_017 |
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- source_sentence: What are the different types of mortgage rate locks available to |
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borrowers? |
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sentences: |
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- freddie_mac_guide |
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- Common rate locks include a 30-day lock, a 45-day lock, and a 60-day lock, with |
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longer locks sometimes incurring a small fee. |
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- freddie_mac_guide_chunk_014 |
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- source_sentence: How do lenders verify a borrowers assets for reserves? |
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sentences: |
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- va_chapter2_eligibility_chunk_001 |
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- Lenders verify assets by obtaining the two most recent monthly statements for |
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all checking, savings, and investment accounts, ensuring the funds have been sourced |
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and seasoned. |
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- va_chapter2_eligibility |
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- source_sentence: When is a borrower eligible for a streamline refinance? |
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sentences: |
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- A borrower is eligible for a streamline refinance if they have made at least six |
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consecutive on-time payments on their current mortgage. |
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- fha_handbook_4000_1 |
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- fha_handbook_4000_1_chunk_007 |
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datasets: |
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- sugiv/mortgage-qa-dataset |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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model-index: |
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- name: SentenceTransformer based on google/embeddinggemma-300m |
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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: mortgage eval |
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type: mortgage-eval |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.34421364985163205 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.6468842729970327 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.7863501483679525 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9317507418397626 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.34421364985163205 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.21562809099901084 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1572700296735905 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09317507418397623 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.34421364985163205 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.6468842729970327 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.7863501483679525 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9317507418397626 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.620931422633939 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.5233208515849467 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.5285370057494337 |
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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: mortgage test |
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type: mortgage-test |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.2781065088757396 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5621301775147929 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.7100591715976331 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8727810650887574 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.2781065088757396 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.18737672583826429 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.14201183431952663 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.08727810650887573 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.2781065088757396 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.5621301775147929 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.7100591715976331 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8727810650887574 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.553056381202397 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.4530313703390625 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.46216201514766103 |
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name: Cosine Map@100 |
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--- |
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# SentenceTransformer based on google/embeddinggemma-300m |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) on the [mortgage-qa-dataset](https://huggingface.co/datasets/sugiv/mortgage-qa-dataset) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) <!-- at revision 64614b0b8b64f0c6c1e52b07e4e9a4e8fe4d2da2 --> |
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- **Maximum Sequence Length:** 2048 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [mortgage-qa-dataset](https://huggingface.co/datasets/sugiv/mortgage-qa-dataset) |
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- **Language:** en |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'}) |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) |
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(3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) |
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(4): Normalize() |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sugiv/embeddinggemma-300m-mortgage") |
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# Run inference |
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queries = [ |
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"When is a borrower eligible for a streamline refinance?", |
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] |
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documents = [ |
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'A borrower is eligible for a streamline refinance if they have made at least six consecutive on-time payments on their current mortgage.', |
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'fha_handbook_4000_1', |
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'fha_handbook_4000_1_chunk_007', |
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] |
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query_embeddings = model.encode_query(queries) |
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document_embeddings = model.encode_document(documents) |
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print(query_embeddings.shape, document_embeddings.shape) |
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# [1, 768] [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(query_embeddings, document_embeddings) |
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print(similarities) |
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# tensor([[ 0.8276, -0.0791, -0.0792]]) |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Datasets: `mortgage-eval` and `mortgage-test` |
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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 | mortgage-eval | mortgage-test | |
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|:--------------------|:--------------|:--------------| |
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| cosine_accuracy@1 | 0.3442 | 0.2781 | |
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| cosine_accuracy@3 | 0.6469 | 0.5621 | |
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| cosine_accuracy@5 | 0.7864 | 0.7101 | |
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| cosine_accuracy@10 | 0.9318 | 0.8728 | |
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| cosine_precision@1 | 0.3442 | 0.2781 | |
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| cosine_precision@3 | 0.2156 | 0.1874 | |
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| cosine_precision@5 | 0.1573 | 0.142 | |
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| cosine_precision@10 | 0.0932 | 0.0873 | |
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| cosine_recall@1 | 0.3442 | 0.2781 | |
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| cosine_recall@3 | 0.6469 | 0.5621 | |
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| cosine_recall@5 | 0.7864 | 0.7101 | |
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| cosine_recall@10 | 0.9318 | 0.8728 | |
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| **cosine_ndcg@10** | **0.6209** | **0.5531** | |
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| cosine_mrr@10 | 0.5233 | 0.453 | |
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| cosine_map@100 | 0.5285 | 0.4622 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### mortgage-qa-dataset |
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* Dataset: [mortgage-qa-dataset](https://huggingface.co/datasets/sugiv/mortgage-qa-dataset) at [de29792](https://huggingface.co/datasets/sugiv/mortgage-qa-dataset/tree/de297929404e24bbfaa5909430b0d325e76419cf) |
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* Size: 2,699 training samples |
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* Columns: <code>question</code>, <code>answer</code>, <code>source_document</code>, and <code>source_chunk</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | question | answer | source_document | source_chunk | |
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | string | string | |
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| details | <ul><li>min: 9 tokens</li><li>mean: 16.15 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 31.67 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 9.99 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 15.99 tokens</li><li>max: 20 tokens</li></ul> | |
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* Samples: |
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| question | answer | source_document | source_chunk | |
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|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------|:------------------------------------------------| |
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| <code>When is a full appraisal required for a mortgage application?</code> | <code>A full appraisal is required for most transactions, but it can be waived for certain streamlined refinance programs if the Loan-to-Value LTV ratio is 90 or less.</code> | <code>fha_handbook_4000_1</code> | <code>fha_handbook_4000_1_chunk_005</code> | |
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| <code>When getting a mortgage, who orders the title insurance for the lender?</code> | <code>While often coordinated by the settlement agent, the lender typically requires and is the ultimate recipient of the lenders title insurance policy to protect their financial interest.</code> | <code>va_chapter4_underwriting</code> | <code>va_chapter4_underwriting_chunk_012</code> | |
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| <code>What components of a loan application does an underwriter assess?</code> | <code>Underwriters analyze the four Cs of credit: Capacity income and DTI, Capital assets and reserves, Collateral property value, and Credit credit history and score.</code> | <code>va_chapter5_processing</code> | <code>va_chapter5_processing_chunk_005</code> | |
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* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim", |
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"mini_batch_size": 32, |
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"gather_across_devices": false |
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} |
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``` |
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### Evaluation Dataset |
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#### mortgage-qa-dataset |
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* Dataset: [mortgage-qa-dataset](https://huggingface.co/datasets/sugiv/mortgage-qa-dataset) at [de29792](https://huggingface.co/datasets/sugiv/mortgage-qa-dataset/tree/de297929404e24bbfaa5909430b0d325e76419cf) |
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* Size: 337 evaluation samples |
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* Columns: <code>question</code>, <code>answer</code>, <code>source_document</code>, and <code>source_chunk</code> |
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* Approximate statistics based on the first 337 samples: |
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| | question | answer | source_document | source_chunk | |
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|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | string | string | |
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| details | <ul><li>min: 9 tokens</li><li>mean: 16.44 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 32.28 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 10.14 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 16.14 tokens</li><li>max: 20 tokens</li></ul> | |
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* Samples: |
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| question | answer | source_document | source_chunk | |
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|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------|:--------------------------------------------------| |
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| <code>What financial metrics are crucial for an AUS to issue an approval?</code> | <code>Key AUS factors include credit score, loan-to-value ratio, debt-to-income ratio, and the overall strength and stability of the borrowers financial profile.</code> | <code>va_chapter4_underwriting</code> | <code>va_chapter4_underwriting_chunk_017</code> | |
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| <code>Can you explain how an LTV ratio is figured out?</code> | <code>The LTV ratio is calculated by dividing the mortgage loan amount by the appraised value or purchase price of the property, whichever is lower.</code> | <code>fannie_mae_servicing_guide</code> | <code>fannie_mae_servicing_guide_chunk_002</code> | |
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| <code>How do lenders verify a borrowers employment history?</code> | <code>Lenders verify employment by contacting employers directly and typically require a two-year history, which may be confirmed via recent pay stubs and W-2 forms.</code> | <code>freddie_mac_guide</code> | <code>freddie_mac_guide_chunk_002</code> | |
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* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim", |
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"mini_batch_size": 32, |
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"gather_across_devices": false |
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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`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `learning_rate`: 3e-06 |
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- `num_train_epochs`: 4 |
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- `warmup_steps`: 100 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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- `batch_sampler`: no_duplicates |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 16 |
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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`: 1 |
|
|
- `eval_accumulation_steps`: None |
|
|
- `torch_empty_cache_steps`: None |
|
|
- `learning_rate`: 3e-06 |
|
|
- `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`: linear |
|
|
- `lr_scheduler_kwargs`: {} |
|
|
- `warmup_ratio`: 0.0 |
|
|
- `warmup_steps`: 100 |
|
|
- `log_level`: passive |
|
|
- `log_level_replica`: warning |
|
|
- `log_on_each_node`: True |
|
|
- `logging_nan_inf_filter`: True |
|
|
- `save_safetensors`: True |
|
|
- `save_on_each_node`: False |
|
|
- `save_only_model`: False |
|
|
- `restore_callback_states_from_checkpoint`: False |
|
|
- `no_cuda`: False |
|
|
- `use_cpu`: False |
|
|
- `use_mps_device`: False |
|
|
- `seed`: 42 |
|
|
- `data_seed`: None |
|
|
- `jit_mode_eval`: False |
|
|
- `use_ipex`: False |
|
|
- `bf16`: False |
|
|
- `fp16`: True |
|
|
- `fp16_opt_level`: O1 |
|
|
- `half_precision_backend`: auto |
|
|
- `bf16_full_eval`: False |
|
|
- `fp16_full_eval`: False |
|
|
- `tf32`: None |
|
|
- `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} |
|
|
- `parallelism_config`: 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`: None |
|
|
- `hub_always_push`: False |
|
|
- `hub_revision`: None |
|
|
- `gradient_checkpointing`: False |
|
|
- `gradient_checkpointing_kwargs`: None |
|
|
- `include_inputs_for_metrics`: False |
|
|
- `include_for_metrics`: [] |
|
|
- `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 |
|
|
- `include_tokens_per_second`: False |
|
|
- `include_num_input_tokens_seen`: False |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: False |
|
|
- `use_liger_kernel`: False |
|
|
- `liger_kernel_config`: None |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
- `router_mapping`: {} |
|
|
- `learning_rate_mapping`: {} |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | Validation Loss | mortgage-eval_cosine_ndcg@10 | mortgage-test_cosine_ndcg@10 | |
|
|
|:----------:|:-------:|:-------------:|:---------------:|:----------------------------:|:----------------------------:| |
|
|
| -1 | -1 | - | - | 0.5803 | - | |
|
|
| 0.1479 | 25 | 0.1574 | - | - | - | |
|
|
| 0.2959 | 50 | 0.1053 | 0.0722 | 0.5993 | - | |
|
|
| 0.4438 | 75 | 0.0969 | - | - | - | |
|
|
| 0.5917 | 100 | 0.0765 | 0.0773 | 0.6085 | - | |
|
|
| 0.7396 | 125 | 0.079 | - | - | - | |
|
|
| 0.8876 | 150 | 0.0802 | 0.0858 | 0.6056 | - | |
|
|
| 1.0355 | 175 | 0.021 | - | - | - | |
|
|
| 1.1834 | 200 | 0.0728 | 0.0549 | 0.6093 | - | |
|
|
| 1.3314 | 225 | 0.0857 | - | - | - | |
|
|
| 1.4793 | 250 | 0.071 | 0.0659 | 0.6145 | - | |
|
|
| 1.6272 | 275 | 0.0633 | - | - | - | |
|
|
| **1.7751** | **300** | **0.1844** | **0.0687** | **0.6209** | **-** | |
|
|
| 1.9231 | 325 | 0.0545 | - | - | - | |
|
|
| 2.0710 | 350 | 0.0474 | 0.0646 | 0.6025 | - | |
|
|
| 2.2189 | 375 | 0.0702 | - | - | - | |
|
|
| 2.3669 | 400 | 0.0831 | 0.0699 | 0.6026 | - | |
|
|
| 2.5148 | 425 | 0.0635 | - | - | - | |
|
|
| 2.6627 | 450 | 0.103 | 0.0674 | 0.6031 | - | |
|
|
| 2.8107 | 475 | 0.097 | - | - | - | |
|
|
| 2.9586 | 500 | 0.077 | 0.0686 | 0.6032 | - | |
|
|
| 3.1065 | 525 | 0.0713 | - | - | - | |
|
|
| 3.2544 | 550 | 0.1617 | 0.0668 | 0.6087 | - | |
|
|
| 3.4024 | 575 | 0.1084 | - | - | - | |
|
|
| 3.5503 | 600 | 0.0791 | 0.0658 | 0.6038 | - | |
|
|
| 3.6982 | 625 | 0.0477 | - | - | - | |
|
|
| 3.8462 | 650 | 0.0956 | 0.0659 | 0.6073 | - | |
|
|
| 3.9941 | 675 | 0.0587 | - | - | - | |
|
|
| -1 | -1 | - | - | 0.6209 | 0.5531 | |
|
|
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.11.11 |
|
|
- Sentence Transformers: 5.1.0 |
|
|
- Transformers: 4.57.0.dev0 |
|
|
- PyTorch: 2.8.0.dev20250319+cu128 |
|
|
- Accelerate: 1.10.1 |
|
|
- Datasets: 4.0.0 |
|
|
- Tokenizers: 0.22.0 |
|
|
|
|
|
## 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", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### CachedMultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{gao2021scaling, |
|
|
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
|
|
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
|
|
year={2021}, |
|
|
eprint={2101.06983}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.LG} |
|
|
} |
|
|
``` |
|
|
|
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