| { |
| "model_type": "SentenceTransformer", |
| "__version__": { |
| "sentence_transformers": "5.1.2", |
| "transformers": "4.56.2", |
| "pytorch": "2.6.0+cu124" |
| }, |
| "prompts": { |
| "EcomRetrieval-query": "Instruct: Given a user query from an e-commerce website, retrieve description sentences of relevant products.\nQuery: ", |
| "MedicalRetrieval-query": "Instruct: Given a medical question, retrieve user replies that best answer the question.\nQuery: ", |
| "CMedQAv1-reranking-query": "Instruct: Given a Chinese community medical question, retrieve replies that best answer the question.\nQuery: ", |
| "CMedQAv2-reranking-query": "Instruct: Given a Chinese community medical question, retrieve replies that best answer the question.\nQuery: ", |
| "ATEC": "Instruct: Retrieve semantically similar text.\nQuery: ", |
| "BQ": "Instruct: Retrieve semantically similar text.\nQuery: ", |
| "LCQMC": "Instruct: Retrieve semantically similar text.\nQuery: ", |
| "PAWSX": "Instruct: Retrieve semantically similar text.\nQuery: ", |
| "STSB": "Instruct: Retrieve semantically similar text.\nQuery: ", |
| "AFQMC": "Instruct: Retrieve semantically similar text.\nQuery: ", |
| "QBQTC": "Instruct: Retrieve semantically similar text.\nQuery: ", |
| "TNews": "Instruct: Classify the fine-grained category of the given news title.\nQuery: ", |
| "IFlyTek": "Instruct: Given an App description text, find the appropriate fine-grained category.\nQuery: ", |
| "MultilingualSentiment": "Instruct: Classify sentiment of the customer review into positive, neutral, or negative.\nQuery: ", |
| "JDReview": "Instruct: Classify the customer review for iPhone on e-commerce platform into positive or negative.\nQuery: ", |
| "OnlineShopping": "Instruct: Classify the customer review for online shopping into positive or negative.\nQuery: ", |
| "Waimai": "Instruct: Classify the customer review from a food takeaway platform into positive or negative.\nQuery: ", |
| "Ocnli": "Instruct: Retrieve semantically similar text.\nQuery: ", |
| "Cmnli": "Instruct: Retrieve semantically similar text.\nQuery: ", |
| "CLSClusteringP2P": "Instruct: Identify the main category of scholar papers based on the titles and abstracts.\nQuery: ", |
| "CLSClusteringS2S": "Instruct: Identify the main category of scholar papers based on the titles.\nQuery: ", |
| "ThuNewsClusteringS2S": "Instruct: Identify the topic or theme of the given news articles based on the titles.\nQuery: ", |
| "T2Retrieval-query": "Instruct: Given a Chinese search query, retrieve web passages that answer the question.\nQuery: ", |
| "CmedqaRetrieval-query": "Instruct: Given a Chinese community medical question, retrieve replies that best answer the question.\nQuery: ", |
| "CovidRetrieval-query": "Instruct: Given a question on COVID-19, retrieve news articles that answer the question.\nQuery: ", |
| "DuRetrieval-query": "Instruct: Given a Chinese search query, retrieve web passages that answer the question.\nQuery: ", |
| "MMarcoReranking-query": "Instruct: Given a Chinese search query, retrieve web passages that answer the question.\nQuery: ", |
| "MMarcoRetrieval-query": "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: ", |
| "T2Reranking-query": "Instruct: Given a Chinese search query, retrieve web passages that answer the question.\nQuery: ", |
| "ThuNewsClusteringP2P": "Instruct: Identify the topic or theme of the given news articles based on the titles and contents.\nQuery: ", |
| "VideoRetrieval-query": "Instruct: Given a video search query, retrieve the titles of relevant videos.\nQuery: " |
| }, |
| "default_prompt_name": null, |
| "similarity_fn_name": "cosine" |
| } |
