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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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- generated_from_trainer |
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- dataset_size:400 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: Snowflake/snowflake-arctic-embed-l |
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widget: |
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- source_sentence: Why should manipulative and exploitative uses of AI be prohibited |
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according to the context provided? |
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sentences: |
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- to operate without human intervention. The adaptiveness that an AI system could |
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exhibit after deployment, refers to self-learning capabilities, allowing the system |
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to change while in use. AI systems can be used on a stand-alone basis or as a component |
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of a product, irrespective of whether the system is physically integrated into |
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the product (embedded) or serves the functionality of the product without being |
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integrated therein (non-embedded). |
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- '(28) |
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Aside from the many beneficial uses of AI, it can also be misused and provide |
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novel and powerful tools for manipulative, exploitative and social control practices. |
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Such practices are particularly harmful and abusive and should be prohibited because |
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they contradict Union values of respect for human dignity, freedom, equality, |
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democracy and the rule of law and fundamental rights enshrined in the Charter, |
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including the right to non-discrimination, to data protection and to privacy and |
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the rights of the child. |
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(29)' |
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- A Union legal framework laying down harmonised rules on AI is therefore needed |
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to foster the development, use and uptake of AI in the internal market that at |
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the same time meets a high level of protection of public interests, such as health |
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and safety and the protection of fundamental rights, including democracy, the |
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rule of law and environmental protection as recognised and protected by Union |
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law. To achieve that objective, rules regulating the placing on the market, the |
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putting into service and the use of certain AI systems should be laid down, thus |
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ensuring the smooth functioning of the internal market and allowing those systems |
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to benefit from the principle of free movement of goods and services. Those rules |
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should be clear and robust |
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- source_sentence: What are the ethical principles mentioned in the context for developing |
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voluntary best practices and standards? |
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sentences: |
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- encouraged to take into account, as appropriate, the ethical principles for the |
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development of voluntary best practices and standards. |
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- completed human activity that may be relevant for the purposes of the high-risk |
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uses listed in an annex to this Regulation. Considering those characteristics, |
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the AI system provides only an additional layer to a human activity with consequently |
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lowered risk. That condition would, for example, apply to AI systems that are |
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intended to improve the language used in previously drafted documents, for example |
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in relation to professional tone, academic style of language or by aligning text |
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to a certain brand messaging. The third condition should be that the AI system |
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is intended to detect decision-making patterns or deviations from prior decision-making |
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patterns. The risk would be lowered because the use of the AI system follows a previously |
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- (17) |
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- source_sentence: How do climate change mitigation and adaptation relate to the conservation |
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of biodiversity? |
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sentences: |
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- of the conditions referred to above should draw up documentation of the assessment |
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before that system is placed on the market or put into service and should provide |
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that documentation to national competent authorities upon request. Such a provider |
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should be obliged to register the AI system in the EU database established under |
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this Regulation. With a view to providing further guidance for the practical implementation |
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of the conditions under which the AI systems listed in an annex to this Regulation |
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are, on an exceptional basis, non-high-risk, the Commission should, after consulting |
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the Board, provide guidelines specifying that practical implementation, completed |
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by a comprehensive list of practical examples of use cases of AI systems that |
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- the conservation and restoration of biodiversity and ecosystems and climate change |
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mitigation and adaptation. |
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- logistical point of view. |
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- source_sentence: How often should the risk-management system be reviewed and updated |
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to maintain its effectiveness? |
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sentences: |
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- The risk-management system should consist of a continuous, iterative process that |
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is planned and run throughout the entire lifecycle of a high-risk AI system. That |
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process should be aimed at identifying and mitigating the relevant risks of AI |
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systems on health, safety and fundamental rights. The risk-management system should |
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be regularly reviewed and updated to ensure its continuing effectiveness, as well |
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as justification and documentation of any significant decisions and actions taken |
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subject to this Regulation. This process should ensure that the provider identifies |
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risks or adverse impacts and implements mitigation measures for the known and |
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reasonably foreseeable risks of AI systems to the health, safety and fundamental |
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rights in light |
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- solely on profiling them or on assessing their personality traits and characteristics |
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should be prohibited. In any case, that prohibition does not refer to or touch |
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upon risk analytics that are not based on the profiling of individuals or on the |
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personality traits and characteristics of individuals, such as AI systems using |
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risk analytics to assess the likelihood of financial fraud by undertakings on |
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the basis of suspicious transactions or risk analytic tools to predict the likelihood |
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of the localisation of narcotics or illicit goods by customs authorities, for |
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example on the basis of known trafficking routes. |
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- be clear and robust in protecting fundamental rights, supportive of new innovative |
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solutions, enabling a European ecosystem of public and private actors creating |
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AI systems in line with Union values and unlocking the potential of the digital |
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transformation across all regions of the Union. By laying down those rules as |
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well as measures in support of innovation with a particular focus on small and |
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medium enterprises (SMEs), including startups, this Regulation supports the objective |
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of promoting the European human-centric approach to AI and being a global leader |
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in the development of secure, trustworthy and ethical AI as stated by the European |
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Council (5), and it ensures the protection of ethical principles, as specifically |
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requested by the |
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- source_sentence: How is the number 42 used in mathematical contexts? |
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sentences: |
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- (65) |
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- (42) |
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- to obtain prior authorisation. This could be, for example, a person involved in |
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a crime, being unwilling, or unable due to an accident or a medical condition, |
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to disclose their identity to law enforcement authorities. |
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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 Snowflake/snowflake-arctic-embed-l |
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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: Unknown |
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type: unknown |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.875 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 1.0 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 1.0 |
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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: 0.875 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.3333333333333333 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.19999999999999998 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.09999999999999999 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.875 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 1.0 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 1.0 |
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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: 0.9484108127976215 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.9305555555555555 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.9305555555555557 |
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name: Cosine Map@100 |
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--- |
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|
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# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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|
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## Model Details |
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|
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 1024 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/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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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 1024, '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("arthikrangan/legal-ft-1") |
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# Run inference |
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sentences = [ |
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'How is the number 42 used in mathematical contexts?', |
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'(42)', |
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'(65)', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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|
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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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* 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 | 0.875 | |
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| cosine_accuracy@3 | 1.0 | |
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| cosine_accuracy@5 | 1.0 | |
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| cosine_accuracy@10 | 1.0 | |
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| cosine_precision@1 | 0.875 | |
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| cosine_precision@3 | 0.3333 | |
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| cosine_precision@5 | 0.2 | |
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| cosine_precision@10 | 0.1 | |
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| cosine_recall@1 | 0.875 | |
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| cosine_recall@3 | 1.0 | |
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| cosine_recall@5 | 1.0 | |
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| cosine_recall@10 | 1.0 | |
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| **cosine_ndcg@10** | **0.9484** | |
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| cosine_mrr@10 | 0.9306 | |
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| cosine_map@100 | 0.9306 | |
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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: 400 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 400 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 10 tokens</li><li>mean: 20.49 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 93.01 tokens</li><li>max: 186 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:-----------------------------------------------------------------------------|:-------------------------------------------------------| |
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| <code>What was requested by the European Parliament?</code> | <code>requested by the European Parliament (6).</code> | |
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| <code>Who made the request to the European Parliament?</code> | <code>requested by the European Parliament (6).</code> | |
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| <code>What is the significance of the number 60 in the given context?</code> | <code>(60)</code> | |
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* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 10 |
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- `per_device_eval_batch_size`: 10 |
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- `num_train_epochs`: 10 |
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- `multi_dataset_batch_sampler`: round_robin |
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|
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#### All Hyperparameters |
|
<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 10 |
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- `per_device_eval_batch_size`: 10 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 10 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
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- `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 |
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- `optim_target_modules`: None |
|
- `batch_eval_metrics`: False |
|
- `eval_on_start`: False |
|
- `use_liger_kernel`: False |
|
- `eval_use_gather_object`: False |
|
- `average_tokens_across_devices`: False |
|
- `prompts`: None |
|
- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
|
|
|
</details> |
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|
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### Training Logs |
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| Epoch | Step | cosine_ndcg@10 | |
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|:-----:|:----:|:--------------:| |
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| 1.0 | 40 | 0.9846 | |
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| 1.25 | 50 | 0.9923 | |
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| 2.0 | 80 | 0.9588 | |
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| 2.5 | 100 | 0.9692 | |
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| 3.0 | 120 | 0.9692 | |
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| 3.75 | 150 | 0.9539 | |
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| 4.0 | 160 | 0.9539 | |
|
| 5.0 | 200 | 0.9588 | |
|
| 6.0 | 240 | 0.9665 | |
|
| 6.25 | 250 | 0.9588 | |
|
| 7.0 | 280 | 0.9511 | |
|
| 7.5 | 300 | 0.9511 | |
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| 8.0 | 320 | 0.9407 | |
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| 8.75 | 350 | 0.9484 | |
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| 9.0 | 360 | 0.9484 | |
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| 10.0 | 400 | 0.9484 | |
|
|
|
|
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### Framework Versions |
|
- Python: 3.11.11 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.48.2 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.3.0 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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|
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## Citation |
|
|
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### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
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