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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-m |
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widget: |
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- source_sentence: What types of objectives are mentioned as not being specific to |
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AI systems in the context? |
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sentences: |
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- The notion of ‘biometric identification’ referred to in this Regulation should |
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be defined as the automated recognition of physical, physiological and behavioural |
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human features such as the face, eye movement, body shape, voice, prosody, gait, |
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posture, heart rate, blood pressure, odour, keystrokes characteristics, for the |
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purpose of establishing an individual’s identity by comparing biometric data of |
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that individual to stored biometric data of individuals in a reference database, |
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irrespective of whether the individual has given its consent or not. This excludes |
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AI systems intended to be used for biometric verification, which includes authentication, |
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whose sole purpose is to confirm that a specific natural person is the person |
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he or she |
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- are not specific to AI systems and pursue other legitimate public interest objectives, |
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should not be affected by this Regulation. |
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- for supervision of the law enforcement and judicial authorities under this Regulation |
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should assess whether those frameworks for cooperation or international agreements |
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include adequate safeguards with respect to the protection of fundamental rights |
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and freedoms of individuals. Recipient national authorities and Union institutions, |
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bodies, offices and agencies making use of such outputs in the Union remain accountable |
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to ensure their use complies with Union law. When those international agreements |
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are revised or new ones are concluded in the future, the contracting parties should |
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make utmost efforts to align those agreements with the requirements of this Regulation. |
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- source_sentence: How does the context relate to the concept of 49? |
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sentences: |
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- (49) |
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- (56) |
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- (25) |
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- source_sentence: How does a serious disruption of critical infrastructure relate |
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to the threat to life or physical safety of individuals? |
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sentences: |
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- or otherwise, for example, public roads and squares, parks, forests, playgrounds. |
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A space should also be classified as being publicly accessible if, regardless |
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of potential capacity or security restrictions, access is subject to certain predetermined |
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conditions which can be fulfilled by an undetermined number of persons, such as |
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the purchase of a ticket or title of transport, prior registration or having a certain |
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age. In contrast, a space should not be considered to be publicly accessible if |
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access is limited to specific and defined natural persons through either Union |
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or national law directly related to public safety or security or through the clear |
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manifestation of will by the person having the relevant authority over the space. |
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The |
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- to highly varying degrees for the practical pursuit of the localisation or identification |
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of a perpetrator or suspect of the different criminal offences listed and having |
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regard to the likely differences in the seriousness, probability and scale of |
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the harm or possible negative consequences. An imminent threat to life or the |
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physical safety of natural persons could also result from a serious disruption |
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of critical infrastructure, as defined in Article 2, point (4) of Directive (EU) |
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2022/2557 of the European Parliament and of the Council (19), where the disruption |
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or destruction of such critical infrastructure would result in an imminent threat |
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to life or the physical safety of a person, including through serious harm to |
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the provision of |
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- As regards high-risk AI systems that are safety components of products or systems, |
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or which are themselves products or systems falling within the scope of Regulation |
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(EC) No 300/2008 of the European Parliament and of the Council (24), Regulation |
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(EU) No 167/2013 of the European Parliament and of the Council (25), Regulation |
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(EU) No 168/2013 of the European Parliament and of the Council (26), Directive |
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2014/90/EU of the European Parliament and of the Council (27), Directive (EU) |
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2016/797 of the European Parliament and of the Council (28), Regulation (EU) 2018/858 |
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of the European Parliament and of the Council (29), Regulation (EU) 2018/1139 |
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of the European Parliament and of the Council (30), and Regulation (EU) 2019/2144 |
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of the European |
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- source_sentence: What specific rights of children are highlighted in Article 24 |
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of the Charter and the United Nations Convention on the Rights of the Child? |
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sentences: |
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- it is important to highlight the fact that children have specific rights as enshrined |
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in Article 24 of the Charter and in the United Nations Convention on the Rights |
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of the Child, further developed in the UNCRC General Comment No 25 as regards |
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the digital environment, both of which require consideration of the children’s |
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vulnerabilities and provision of such protection and care as necessary for their |
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well-being. The fundamental right to a high level of environmental protection |
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enshrined in the Charter and implemented in Union policies should also be considered |
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when assessing the severity of the harm that an AI system can cause, including |
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in relation to the health and safety of persons. |
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- of AI systems that are high-risk and use cases that are not. |
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- As regards high-risk AI systems that are safety components of products or systems, |
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or which are themselves products or systems falling within the scope of Regulation |
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(EC) No 300/2008 of the European Parliament and of the Council (24), Regulation |
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(EU) No 167/2013 of the European Parliament and of the Council (25), Regulation |
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(EU) No 168/2013 of the European Parliament and of the Council (26), Directive |
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2014/90/EU of the European Parliament and of the Council (27), Directive (EU) |
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2016/797 of the European Parliament and of the Council (28), Regulation (EU) 2018/858 |
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of the European Parliament and of the Council (29), Regulation (EU) 2018/1139 |
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of the European Parliament and of the Council (30), and Regulation (EU) 2019/2144 |
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of the European |
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- source_sentence: What is the significance of the number 4 in the provided context? |
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sentences: |
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- are intended to be used solely for the purpose of enabling cybersecurity and personal |
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data protection measures should not be considered to be high-risk AI systems. |
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- (4) |
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- '(5) |
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At the same time, depending on the circumstances regarding its specific application, |
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use, and level of technological development, AI may generate risks and cause harm |
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to public interests and fundamental rights that are protected by Union law. Such |
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harm might be material or immaterial, including physical, psychological, societal |
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or economic harm. |
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(6)' |
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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-m |
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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: 0.9375 |
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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: 0.9375 |
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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: 0.9375 |
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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: 0.9742054063988107 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.9652777777777777 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.9652777777777778 |
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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-m |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). 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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|
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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-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision fc74610d18462d218e312aa986ec5c8a75a98152 --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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### Model Sources |
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|
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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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|
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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': 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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|
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## Usage |
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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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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("Mdean77/legal-ft-1") |
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# Run inference |
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sentences = [ |
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'What is the significance of the number 4 in the provided context?', |
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'(4)', |
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'are intended to be used solely for the purpose of enabling cybersecurity and personal data protection measures should not be considered to be high-risk AI systems.', |
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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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|
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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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|
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## Evaluation |
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### Metrics |
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|
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#### Information Retrieval |
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|
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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 | 0.9375 | |
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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 | 0.9375 | |
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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 | 0.9375 | |
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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** | **0.9742** | |
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| cosine_mrr@10 | 0.9653 | |
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| cosine_map@100 | 0.9653 | |
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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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|
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## Training Details |
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|
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### Training Dataset |
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#### Unnamed Dataset |
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|
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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: 20.43 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 is the significance of the number 50 in the given context?</code> | <code>(50)</code> | |
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| <code>How does the context relate to the concept of fifty?</code> | <code>(50)</code> | |
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| <code>What are the ethical principles mentioned in the context for developing voluntary best practices and standards?</code> | <code>encouraged to take into account, as appropriate, the ethical principles for the development of voluntary best practices and standards.</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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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 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 |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: 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 |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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|
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</details> |
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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 | 0.9503 | |
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| 1.25 | 50 | 0.9547 | |
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| 2.0 | 80 | 0.9742 | |
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| 2.5 | 100 | 0.9728 | |
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| 3.0 | 120 | 0.9742 | |
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| 3.75 | 150 | 0.9692 | |
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| 4.0 | 160 | 0.9769 | |
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| 5.0 | 200 | 0.9692 | |
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| 6.0 | 240 | 0.9742 | |
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| 6.25 | 250 | 0.9742 | |
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| 7.0 | 280 | 0.9665 | |
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| 7.5 | 300 | 0.9665 | |
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| 8.0 | 320 | 0.9692 | |
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| 8.75 | 350 | 0.9665 | |
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| 9.0 | 360 | 0.9665 | |
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| 10.0 | 400 | 0.9742 | |
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### Framework Versions |
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- Python: 3.13.0 |
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- Sentence Transformers: 3.4.1 |
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- Transformers: 4.48.3 |
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- PyTorch: 2.6.0 |
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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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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MatryoshkaLoss |
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```bibtex |
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@misc{kusupati2024matryoshka, |
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title={Matryoshka Representation Learning}, |
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author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
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year={2024}, |
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eprint={2205.13147}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG} |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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