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--- |
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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:713743 |
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- loss:MultipleNegativesRankingLoss |
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base_model: Alibaba-NLP/gte-modernbert-base |
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widget: |
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- source_sentence: 'Abraham Lincoln: Why is the Gettysburg Address so memorable?' |
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sentences: |
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- 'Abraham Lincoln: Why is the Gettysburg Address so memorable?' |
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- What does the Gettysburg Address really mean? |
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- What is eatalo.com? |
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- source_sentence: Has the influence of Ancient Carthage in science, math, and society |
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been underestimated? |
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sentences: |
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- How does one earn money online without an investment from home? |
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- Has the influence of Ancient Carthage in science, math, and society been underestimated? |
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- Has the influence of the Ancient Etruscans in science and math been underestimated? |
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- source_sentence: Is there any app that shares charging to others like share it how |
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we transfer files? |
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sentences: |
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- How do you think of Chinese claims that the present Private Arbitration is illegal, |
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its verdict violates the UNCLOS and is illegal? |
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- Is there any app that shares charging to others like share it how we transfer |
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files? |
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- Are there any platforms that provides end-to-end encryption for file transfer/ |
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sharing? |
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- source_sentence: Why AAP’s MLA Dinesh Mohaniya has been arrested? |
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sentences: |
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- What are your views on the latest sex scandal by AAP MLA Sandeep Kumar? |
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- What is a dc current? What are some examples? |
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- Why AAP’s MLA Dinesh Mohaniya has been arrested? |
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- source_sentence: What is the difference between economic growth and economic development? |
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sentences: |
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- How cold can the Gobi Desert get, and how do its average temperatures compare |
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to the ones in the Simpson Desert? |
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- the difference between economic growth and economic development is What? |
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- What is the difference between economic growth and economic development? |
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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 Alibaba-NLP/gte-modernbert-base |
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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: NanoMSMARCO |
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type: NanoMSMARCO |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.38 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.54 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.68 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.8 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.38 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.18 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.136 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.08 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.38 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.54 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.68 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.5686686381597302 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.49702380952380953 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.5063338862610184 |
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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: NanoNQ |
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type: NanoNQ |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.4 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.56 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.6 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.66 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.4 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.12800000000000003 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.07 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.36 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.54 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.58 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.63 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.5105228253020769 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.48852380952380947 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.4728184565167554 |
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name: Cosine Map@100 |
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- task: |
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type: nano-beir |
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name: Nano BEIR |
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dataset: |
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name: NanoBEIR mean |
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type: NanoBEIR_mean |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.39 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.55 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.64 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.73 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.39 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.19 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.132 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.07500000000000001 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.37 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.54 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.63 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.7150000000000001 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.5395957317309036 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.4927738095238095 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.48957617138888687 |
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name: Cosine Map@100 |
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--- |
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# SentenceTransformer based on Alibaba-NLP/gte-modernbert-base |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base). 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:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision e7f32e3c00f91d699e8c43b53106206bcc72bb22 --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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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/huggingface/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': 128, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) |
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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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) |
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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("redis/model-b-structured") |
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# Run inference |
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sentences = [ |
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'What is the difference between economic growth and economic development?', |
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'What is the difference between economic growth and economic development?', |
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'the difference between economic growth and economic development is What?', |
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] |
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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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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities) |
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# tensor([[ 1.0000, 1.0000, -0.0629], |
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# [ 1.0000, 1.0000, -0.0629], |
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# [-0.0629, -0.0629, 1.0001]]) |
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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: `NanoMSMARCO` and `NanoNQ` |
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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 | NanoMSMARCO | NanoNQ | |
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|:--------------------|:------------|:-----------| |
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| cosine_accuracy@1 | 0.38 | 0.4 | |
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| cosine_accuracy@3 | 0.54 | 0.56 | |
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| cosine_accuracy@5 | 0.68 | 0.6 | |
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| cosine_accuracy@10 | 0.8 | 0.66 | |
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| cosine_precision@1 | 0.38 | 0.4 | |
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| cosine_precision@3 | 0.18 | 0.2 | |
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| cosine_precision@5 | 0.136 | 0.128 | |
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| cosine_precision@10 | 0.08 | 0.07 | |
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| cosine_recall@1 | 0.38 | 0.36 | |
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| cosine_recall@3 | 0.54 | 0.54 | |
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| cosine_recall@5 | 0.68 | 0.58 | |
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| cosine_recall@10 | 0.8 | 0.63 | |
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| **cosine_ndcg@10** | **0.5687** | **0.5105** | |
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| cosine_mrr@10 | 0.497 | 0.4885 | |
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| cosine_map@100 | 0.5063 | 0.4728 | |
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#### Nano BEIR |
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* Dataset: `NanoBEIR_mean` |
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* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters: |
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```json |
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{ |
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"dataset_names": [ |
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"msmarco", |
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"nq" |
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], |
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"dataset_id": "lightonai/NanoBEIR-en" |
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} |
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``` |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.39 | |
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| cosine_accuracy@3 | 0.55 | |
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| cosine_accuracy@5 | 0.64 | |
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| cosine_accuracy@10 | 0.73 | |
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| cosine_precision@1 | 0.39 | |
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| cosine_precision@3 | 0.19 | |
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| cosine_precision@5 | 0.132 | |
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| cosine_precision@10 | 0.075 | |
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| cosine_recall@1 | 0.37 | |
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| cosine_recall@3 | 0.54 | |
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| cosine_recall@5 | 0.63 | |
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| cosine_recall@10 | 0.715 | |
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| **cosine_ndcg@10** | **0.5396** | |
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| cosine_mrr@10 | 0.4928 | |
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| cosine_map@100 | 0.4896 | |
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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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#### Unnamed Dataset |
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* Size: 713,743 training samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 15.96 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.93 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.72 tokens</li><li>max: 59 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------| |
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| <code>Which one is better Linux OS? Ubuntu or Mint?</code> | <code>Why do you use Linux Mint?</code> | <code>Which one is not better Linux OS ? Ubuntu or Mint ?</code> | |
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| <code>What is flow?</code> | <code>What is flow?</code> | <code>What are flow lines?</code> | |
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| <code>How is Trump planning to get Mexico to pay for his supposed wall?</code> | <code>How is it possible for Donald Trump to force Mexico to pay for the wall?</code> | <code>Why do we connect the positive terminal before the negative terminal to ground in a vehicle battery?</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 7.0, |
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"similarity_fct": "cos_sim", |
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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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#### Unnamed Dataset |
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* Size: 40,000 evaluation samples |
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* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | anchor | positive | negative | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
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| type | string | string | string | |
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| details | <ul><li>min: 7 tokens</li><li>mean: 15.47 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.48 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.76 tokens</li><li>max: 67 tokens</li></ul> | |
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* Samples: |
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| anchor | positive | negative | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Why are all my questions on Quora marked needing improvement?</code> | <code>Why are all my questions immediately being marked as needing improvement?</code> | <code>For a post-graduate student in IIT, is it allowed to take an external scholarship as a top-up to his/her MHRD assistantship?</code> | |
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| <code>Can blue butter fly needle with vaccum tube be reused? Is it HIV risk? . Heard the needle is too small to be reused . Had blood draw at clinic?</code> | <code>Can blue butter fly needle with vaccum tube be reused? Is it HIV risk? . Heard the needle is too small to be reused . Had blood draw at clinic?</code> | <code>Can blue butter fly needle with vaccum tube be reused not ? Is it HIV risk ? . Heard the needle is too small to be reused . Had blood draw at clinic ?</code> | |
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| <code>Why do people still believe the world is flat?</code> | <code>Why are there still people who believe the world is flat?</code> | <code>I'm not able to buy Udemy course .it is not accepting mine and my friends debit card.my card can be used for Flipkart .how to purchase now?</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 7.0, |
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"similarity_fct": "cos_sim", |
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"gather_across_devices": false |
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} |
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``` |
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|
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### Training Hyperparameters |
|
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#### Non-Default Hyperparameters |
|
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|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0001 |
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- `max_steps`: 5000 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `dataloader_drop_last`: True |
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- `dataloader_num_workers`: 1 |
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- `dataloader_prefetch_factor`: 1 |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch |
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- `ddp_find_unused_parameters`: False |
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- `push_to_hub`: True |
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- `hub_model_id`: redis/model-b-structured |
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- `eval_on_start`: True |
|
|
|
|
|
#### All Hyperparameters |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
|
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- `eval_strategy`: steps |
|
|
- `prediction_loss_only`: True |
|
|
- `per_device_train_batch_size`: 128 |
|
|
- `per_device_eval_batch_size`: 128 |
|
|
- `per_gpu_train_batch_size`: None |
|
|
- `per_gpu_eval_batch_size`: None |
|
|
- `gradient_accumulation_steps`: 1 |
|
|
- `eval_accumulation_steps`: None |
|
|
- `torch_empty_cache_steps`: None |
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- `learning_rate`: 2e-05 |
|
|
- `weight_decay`: 0.0001 |
|
|
- `adam_beta1`: 0.9 |
|
|
- `adam_beta2`: 0.999 |
|
|
- `adam_epsilon`: 1e-08 |
|
|
- `max_grad_norm`: 1.0 |
|
|
- `num_train_epochs`: 3.0 |
|
|
- `max_steps`: 5000 |
|
|
- `lr_scheduler_type`: linear |
|
|
- `lr_scheduler_kwargs`: {} |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `warmup_steps`: 0 |
|
|
- `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 |
|
|
- `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`: True |
|
|
- `dataloader_num_workers`: 1 |
|
|
- `dataloader_prefetch_factor`: 1 |
|
|
- `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 |
|
|
- `optim_args`: None |
|
|
- `adafactor`: False |
|
|
- `group_by_length`: False |
|
|
- `length_column_name`: length |
|
|
- `project`: huggingface |
|
|
- `trackio_space_id`: trackio |
|
|
- `ddp_find_unused_parameters`: False |
|
|
- `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`: True |
|
|
- `resume_from_checkpoint`: None |
|
|
- `hub_model_id`: redis/model-b-structured |
|
|
- `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`: no |
|
|
- `neftune_noise_alpha`: None |
|
|
- `optim_target_modules`: None |
|
|
- `batch_eval_metrics`: False |
|
|
- `eval_on_start`: True |
|
|
- `use_liger_kernel`: False |
|
|
- `liger_kernel_config`: None |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: True |
|
|
- `prompts`: None |
|
|
- `batch_sampler`: batch_sampler |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
- `router_mapping`: {} |
|
|
- `learning_rate_mapping`: {} |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |
|
|
|:------:|:----:|:-------------:|:---------------:|:--------------------------:|:---------------------:|:----------------------------:| |
|
|
| 0 | 0 | - | 2.2389 | 0.6530 | 0.6552 | 0.6541 | |
|
|
| 0.0448 | 250 | 1.0022 | 0.4154 | 0.6615 | 0.5429 | 0.6022 | |
|
|
| 0.0897 | 500 | 0.3871 | 0.3658 | 0.6042 | 0.4458 | 0.5250 | |
|
|
| 0.1345 | 750 | 0.3575 | 0.3479 | 0.5819 | 0.5160 | 0.5489 | |
|
|
| 0.1793 | 1000 | 0.3454 | 0.3355 | 0.5976 | 0.5595 | 0.5785 | |
|
|
| 0.2242 | 1250 | 0.337 | 0.3284 | 0.5901 | 0.4544 | 0.5223 | |
|
|
| 0.2690 | 1500 | 0.3291 | 0.3235 | 0.6138 | 0.5729 | 0.5933 | |
|
|
| 0.3138 | 1750 | 0.323 | 0.3182 | 0.6210 | 0.5608 | 0.5909 | |
|
|
| 0.3587 | 2000 | 0.3206 | 0.3141 | 0.6139 | 0.5474 | 0.5807 | |
|
|
| 0.4035 | 2250 | 0.3151 | 0.3120 | 0.6275 | 0.5665 | 0.5970 | |
|
|
| 0.4484 | 2500 | 0.3132 | 0.3093 | 0.6059 | 0.5349 | 0.5704 | |
|
|
| 0.4932 | 2750 | 0.3087 | 0.3072 | 0.6011 | 0.5305 | 0.5658 | |
|
|
| 0.5380 | 3000 | 0.3065 | 0.3051 | 0.5816 | 0.5057 | 0.5436 | |
|
|
| 0.5829 | 3250 | 0.3044 | 0.3033 | 0.5959 | 0.5203 | 0.5581 | |
|
|
| 0.6277 | 3500 | 0.3053 | 0.3018 | 0.5817 | 0.5185 | 0.5501 | |
|
|
| 0.6725 | 3750 | 0.3028 | 0.3006 | 0.5744 | 0.5052 | 0.5398 | |
|
|
| 0.7174 | 4000 | 0.3018 | 0.2996 | 0.5783 | 0.5190 | 0.5487 | |
|
|
| 0.7622 | 4250 | 0.3011 | 0.2994 | 0.5679 | 0.4959 | 0.5319 | |
|
|
| 0.8070 | 4500 | 0.3009 | 0.2979 | 0.5689 | 0.5068 | 0.5378 | |
|
|
| 0.8519 | 4750 | 0.2985 | 0.2975 | 0.5687 | 0.5135 | 0.5411 | |
|
|
| 0.8967 | 5000 | 0.2995 | 0.2971 | 0.5687 | 0.5105 | 0.5396 | |
|
|
|
|
|
|
|
|
### Framework Versions |
|
|
- Python: 3.10.18 |
|
|
- Sentence Transformers: 5.2.0 |
|
|
- Transformers: 4.57.3 |
|
|
- PyTorch: 2.9.1+cu128 |
|
|
- Accelerate: 1.12.0 |
|
|
- Datasets: 2.21.0 |
|
|
- Tokenizers: 0.22.1 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### MultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{henderson2017efficient, |
|
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
|
year={2017}, |
|
|
eprint={1705.00652}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.CL} |
|
|
} |
|
|
``` |
|
|
|
|
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