How to use amentaphd/snowflake-artic-embed-l with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("amentaphd/snowflake-artic-embed-l") sentences = [ "What criteria must Member States consider when establishing penalties for infringements of the specified Regulation, and what is the deadline for notifying the Commission about these rules?", "Enforcement\n\n1.\n\nMember States shall lay down the rules on penalties applicable to infringements of this Regulation and shall take all measures necessary to ensure that they are implemented. The penalties provided for must be effective, proportionate and dissuasive taking into account, in particular, the nature, duration, recurrence and gravity of the infringement. Member States shall, by 31 December 2024, notify the Commission of those rules and of those measures and shall notify it without delay of any subsequent amendment affecting them.\n\n2.", "Within the transitional periods established, Member States shall progressively reduce their respective gaps with regard to the new minimum levels of taxation. However, where the difference between the national level and the minimum level does not exceed 3 % of that minimum level, the Member State concerned may wait until the end of the period to adjust its national level.", "AR 10. ‘Indirect political contribution’ refers to those political contributions made through an intermediary organisation such as a lobbyist or charity, or support given to an organisation such as a think tank or trade association linked to or supporting particular political parties or causes.\n\nAR 11. When determining ‘comparable position’ in this standard, the undertaking shall consider various factors, including level of responsibility and scope of activities undertaken.\n\nAR 12. The undertaking may provide the following information on its financial or in-kind contributions with regard to its lobbying expenses:\n\n(a)\n\nthe total monetary amount of such internal and external expenses; and\n\n(b)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
