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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:161 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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widget: |
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- source_sentence: 'As per Part II of the PDPA, Personal Data Protection Commission |
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(PDPC) is the |
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regulatory body to enforce the provisions of PDPA. The PDPC is empowered with |
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broad discretion to issue remedial directions, initiate investigation |
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inquiries, and impose fines and penalties on the organisations in case of any |
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non-compliance of PDPA. |
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1 |
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If organisations misuse the personal data or hide information concerning its |
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collection, use, or disclosure, PDPA states penalties not exceeding **S$50,000 |
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(approx. $36,000)**. |
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2 |
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Penalty for hindering a PDPC investigation can lead to a fine of not more than |
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**S$100,000 (approx. $72,000)**. The PDPA states that companies are also |
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liable for their employees’ actions, whether they are aware of them or not. |
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3 |
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New amendments to PDPA have enforced increased financial penalties for |
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breaches of the PDPA up to **10%** of annual gross turnover in Singapore, or |
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**S$ 1 million** , whichever is higher. |
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4 |
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Non-compliance with specific provisions under the PDPA may also constitute an |
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offense, for which a fine or a term of **imprisonment** may be imposed. |
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5 |
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An individual can bring a private civil action against an organisation for |
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having suffered **loss or damage** directly due to a contravention of the |
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provisions of the PDPA.' |
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sentences: |
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- What is the right to notice under the CCPA? |
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- What are the risks of non-compliance with the PDPA? |
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- What is the definition of personal data under the PDP Law? |
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- source_sentence: The DPA requires all data controllers to take appropriate technical |
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and organisational measures that are necessary to protect data from unauthorised |
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destruction, negligent loss, unauthorised alteration or access and any other unauthorised |
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processing of the data. |
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sentences: |
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- Which regulatory authority enforces GDPR in France? |
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- What are the security requirements under the DPA? |
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- How do PIPEDA and GDPR differ? |
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- source_sentence: if the data controller or the data processor holds a valid registration |
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certificate authorizing him or her to store personal data outside Rwanda |
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sentences: |
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- What is the difference between GDPR and a Data Protection Act? |
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- What is the voluntary certification by the CPPA? |
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- Where is personal data storage outside of Rwanda permitted? |
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- source_sentence: The PDP law will regulate sensitive personal data as well as other |
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personal data that may endanger or harm the privacy of the data subject. |
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sentences: |
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- What is the material scope of the PDP Law? |
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- What is the definition of personal information under the DPA in the Philippines? |
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- What does Securiti offer to help with data privacy compliance? |
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- source_sentence: Thailand's PDPA applies to any legal entity collecting, using, |
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or disclosing a natural (and alive) person's personal data. |
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sentences: |
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- Who does the Thailand's PDPA apply to? |
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- What penalties could an organization face for infringing Kenya's Data Protection |
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Act? |
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- What is the CPRA? |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on BAAI/bge-base-en-v1.5 |
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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: 0.5555555555555556 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8333333333333334 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8888888888888888 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5555555555555556 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27777777777777773 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17777777777777778 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.10000000000000002 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5555555555555556 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8333333333333334 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8888888888888888 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 1.0 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7730002998303461 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7011463844797178 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7011463844797178 |
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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: 0.5555555555555556 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8333333333333334 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8888888888888888 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5555555555555556 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27777777777777773 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.17777777777777778 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.10000000000000002 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5555555555555556 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8333333333333334 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8888888888888888 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 1.0 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7730002998303461 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7011463844797178 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7011463844797178 |
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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: 0.5555555555555556 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8888888888888888 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.9444444444444444 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5555555555555556 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2962962962962962 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1888888888888889 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.10000000000000002 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5555555555555556 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8888888888888888 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.9444444444444444 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 1.0 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7903353721281168 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7217592592592593 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7217592592592593 |
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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: 0.5555555555555556 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.8333333333333334 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8888888888888888 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.9444444444444444 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.5555555555555556 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.27777777777777773 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.1777777777777778 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09444444444444446 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.5555555555555556 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.8333333333333334 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8888888888888888 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.9444444444444444 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.7641903093346225 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.7052469135802469 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7080246913580247 |
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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: 0.4444444444444444 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.6666666666666666 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.8333333333333334 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 1.0 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.4444444444444444 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.2222222222222222 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.16666666666666669 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.10000000000000002 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.4444444444444444 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.6666666666666666 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8333333333333334 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 1.0 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.6976955584560773 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.6044753086419753 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
|
value: 0.6044753086419754 |
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name: Cosine Map@100 |
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--- |
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# SentenceTransformer based on BAAI/bge-base-en-v1.5 |
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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 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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- **Language:** en |
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- **License:** apache-2.0 |
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### 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': 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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``` |
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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("MugheesAwan11/bge-base-securiti-dataset-1-v3") |
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# Run inference |
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sentences = [ |
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"Thailand's PDPA applies to any legal entity collecting, using, or disclosing a natural (and alive) person's personal data.", |
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"Who does the Thailand's PDPA apply to?", |
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"What penalties could an organization face for infringing Kenya's Data Protection Act?", |
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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.shape) |
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# [3, 3] |
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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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* 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 | 0.5556 | |
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| cosine_accuracy@3 | 0.8333 | |
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| cosine_accuracy@5 | 0.8889 | |
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| cosine_accuracy@10 | 1.0 | |
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| cosine_precision@1 | 0.5556 | |
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| cosine_precision@3 | 0.2778 | |
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| cosine_precision@5 | 0.1778 | |
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| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.5556 | |
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| cosine_recall@3 | 0.8333 | |
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| cosine_recall@5 | 0.8889 | |
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| cosine_recall@10 | 1.0 | |
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| cosine_ndcg@10 | 0.773 | |
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| cosine_mrr@10 | 0.7011 | |
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| **cosine_map@100** | **0.7011** | |
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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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|
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.5556 | |
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| cosine_accuracy@3 | 0.8333 | |
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| cosine_accuracy@5 | 0.8889 | |
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| cosine_accuracy@10 | 1.0 | |
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| cosine_precision@1 | 0.5556 | |
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| cosine_precision@3 | 0.2778 | |
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| cosine_precision@5 | 0.1778 | |
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| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.5556 | |
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| cosine_recall@3 | 0.8333 | |
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| cosine_recall@5 | 0.8889 | |
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| cosine_recall@10 | 1.0 | |
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| cosine_ndcg@10 | 0.773 | |
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| cosine_mrr@10 | 0.7011 | |
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| **cosine_map@100** | **0.7011** | |
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|
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#### 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 | 0.5556 | |
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| cosine_accuracy@3 | 0.8889 | |
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| cosine_accuracy@5 | 0.9444 | |
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| cosine_accuracy@10 | 1.0 | |
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| cosine_precision@1 | 0.5556 | |
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| cosine_precision@3 | 0.2963 | |
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| cosine_precision@5 | 0.1889 | |
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| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.5556 | |
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| cosine_recall@3 | 0.8889 | |
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| cosine_recall@5 | 0.9444 | |
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| cosine_recall@10 | 1.0 | |
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| cosine_ndcg@10 | 0.7903 | |
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| cosine_mrr@10 | 0.7218 | |
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| **cosine_map@100** | **0.7218** | |
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|
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#### 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) |
|
|
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| Metric | Value | |
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|:--------------------|:----------| |
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| cosine_accuracy@1 | 0.5556 | |
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| cosine_accuracy@3 | 0.8333 | |
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| cosine_accuracy@5 | 0.8889 | |
|
| cosine_accuracy@10 | 0.9444 | |
|
| cosine_precision@1 | 0.5556 | |
|
| cosine_precision@3 | 0.2778 | |
|
| cosine_precision@5 | 0.1778 | |
|
| cosine_precision@10 | 0.0944 | |
|
| cosine_recall@1 | 0.5556 | |
|
| cosine_recall@3 | 0.8333 | |
|
| cosine_recall@5 | 0.8889 | |
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| cosine_recall@10 | 0.9444 | |
|
| cosine_ndcg@10 | 0.7642 | |
|
| cosine_mrr@10 | 0.7052 | |
|
| **cosine_map@100** | **0.708** | |
|
|
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#### Information Retrieval |
|
* Dataset: `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.4444 | |
|
| cosine_accuracy@3 | 0.6667 | |
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| cosine_accuracy@5 | 0.8333 | |
|
| cosine_accuracy@10 | 1.0 | |
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| cosine_precision@1 | 0.4444 | |
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| cosine_precision@3 | 0.2222 | |
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| cosine_precision@5 | 0.1667 | |
|
| cosine_precision@10 | 0.1 | |
|
| cosine_recall@1 | 0.4444 | |
|
| cosine_recall@3 | 0.6667 | |
|
| cosine_recall@5 | 0.8333 | |
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| cosine_recall@10 | 1.0 | |
|
| cosine_ndcg@10 | 0.6977 | |
|
| cosine_mrr@10 | 0.6045 | |
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| **cosine_map@100** | **0.6045** | |
|
|
|
<!-- |
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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 |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
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|
|
|
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* Size: 161 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: |
|
| | positive | anchor | |
|
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 5 tokens</li><li>mean: 40.09 tokens</li><li>max: 481 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 13.01 tokens</li><li>max: 24 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------| |
|
| <code>The DPA may impose administrative fines of up to €10 million, or up to 2%<br>of<br>worldwide turnover. The DPA may also impose heavier fines up to €20 million,<br>or up to 4% of worldwide turnover.</code> | <code>What is the penalty for non-compliance with the GDPR in Italy?</code> | |
|
| <code>As per the DPA, the data handler must seek consent in writing from the data subject to collect any sensitive personal data.</code> | <code>What are the consent requirements under the DPA?</code> | |
|
| <code>China's cybersecurity laws include the Cybersecurity Law, which governs<br>various aspects of cybersecurity, data protection, and the obligations of<br>organizations to ensure the security of networks and data within China's<br>territory.</code> | <code>What are the cybersecurity laws in China?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```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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|
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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`: 2 |
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- `learning_rate`: 2e-05 |
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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 |
|
|
|
#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
|
- `do_predict`: False |
|
- `eval_strategy`: epoch |
|
- `prediction_loss_only`: True |
|
- `per_device_train_batch_size`: 32 |
|
- `per_device_eval_batch_size`: 16 |
|
- `per_gpu_train_batch_size`: None |
|
- `per_gpu_eval_batch_size`: None |
|
- `gradient_accumulation_steps`: 2 |
|
- `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 |
|
- `max_grad_norm`: 1.0 |
|
- `num_train_epochs`: 3 |
|
- `max_steps`: -1 |
|
- `lr_scheduler_type`: cosine |
|
- `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 |
|
- `use_ipex`: False |
|
- `bf16`: True |
|
- `fp16`: False |
|
- `fp16_opt_level`: O1 |
|
- `half_precision_backend`: auto |
|
- `bf16_full_eval`: False |
|
- `fp16_full_eval`: False |
|
- `tf32`: True |
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- `local_rank`: 0 |
|
- `ddp_backend`: None |
|
- `tpu_num_cores`: None |
|
- `tpu_metrics_debug`: False |
|
- `debug`: [] |
|
- `dataloader_drop_last`: False |
|
- `dataloader_num_workers`: 0 |
|
- `dataloader_prefetch_factor`: None |
|
- `past_index`: -1 |
|
- `disable_tqdm`: False |
|
- `remove_unused_columns`: True |
|
- `label_names`: None |
|
- `load_best_model_at_end`: True |
|
- `ignore_data_skip`: False |
|
- `fsdp`: [] |
|
- `fsdp_min_num_params`: 0 |
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
- `deepspeed`: None |
|
- `label_smoothing_factor`: 0.0 |
|
- `optim`: adamw_torch_fused |
|
- `optim_args`: None |
|
- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
|
- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
|
- `use_legacy_prediction_loop`: False |
|
- `push_to_hub`: False |
|
- `resume_from_checkpoint`: None |
|
- `hub_model_id`: None |
|
- `hub_strategy`: every_save |
|
- `hub_private_repo`: False |
|
- `hub_always_push`: False |
|
- `gradient_checkpointing`: False |
|
- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
|
- `eval_do_concat_batches`: True |
|
- `fp16_backend`: auto |
|
- `push_to_hub_model_id`: None |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
|
- `full_determinism`: False |
|
- `torchdynamo`: None |
|
- `ray_scope`: last |
|
- `ddp_timeout`: 1800 |
|
- `torch_compile`: False |
|
- `torch_compile_backend`: None |
|
- `torch_compile_mode`: None |
|
- `dispatch_batches`: None |
|
- `split_batches`: None |
|
- `include_tokens_per_second`: False |
|
- `include_num_input_tokens_seen`: False |
|
- `neftune_noise_alpha`: None |
|
- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `batch_sampler`: no_duplicates |
|
- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
|
|
|
### Training Logs |
|
| Epoch | Step | 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 | |
|
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 1.0 | 3 | 0.6510 | 0.6691 | 0.6534 | 0.5641 | 0.6515 | |
|
| **2.0** | **6** | **0.6605** | **0.679** | **0.6627** | **0.5768** | **0.6515** | |
|
| 1.0 | 3 | 0.6702 | 0.6914 | 0.6747 | 0.6014 | 0.7043 | |
|
| **2.0** | **6** | **0.7078** | **0.694** | **0.7011** | **0.6052** | **0.7025** | |
|
| 3.0 | 9 | 0.7080 | 0.7218 | 0.7011 | 0.6045 | 0.7011 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.14 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.31.0 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
<!-- |
|
## Glossary |
|
|
|
*Clearly define terms in order to be accessible across audiences.* |
|
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|
|
|
<!-- |
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## Model Card Authors |
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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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