Feature Extraction
Transformers
Safetensors
sentence-transformers
Chinese
English
mteb
custom_code
Eval Results (legacy)
Instructions to use openbmb/MiniCPM-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="openbmb/MiniCPM-Embedding", trust_remote_code=True)# Load model directly from transformers import MiniCPM model = MiniCPM.from_pretrained("openbmb/MiniCPM-Embedding", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use openbmb/MiniCPM-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("openbmb/MiniCPM-Embedding", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 2,514 Bytes
8c02c7a 07ac309 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"fiqa": "Instruction: Given a financial question, retrieve user replies that best answer the question. Query: ",
"dbpedia": "Instruction: Given a query, retrieve relevant entity descriptions from DBPedia. Query: ",
"CmedqaRetrieval": "Instruction: 为这个医疗问题检索相关回答。 Query: ",
"nfcorpus": "Instruction: Given a question, retrieve relevant documents that best answer the question. Query: ",
"touche2020": "Instruction: Given a question, retrieve detailed and persuasive arguments that answer the question. Query: ",
"CovidRetrieval": "Instruction: 为这个问题检索相关政策回答。 Query: ",
"scifact": "Instruction: Given a scientific claim, retrieve documents that support or refute the claim. Query: ",
"scidocs": "Instruction: Given a scientific paper title, retrieve paper abstracts that are cited by the given paper. Query: ",
"nq": "Instruction: Given a question, retrieve Wikipedia passages that answer the question. Query: ",
"T2Retrieval": "Instruction: 为这个问题检索相关段落。 Query: ",
"VideoRetrieval": "Instruction: 为这个电影标题检索相关段落。 Query: ",
"DuRetrieval": "Instruction: 为这个问题检索相关百度知道回答。 Query: ",
"MMarcoRetrieval": "Instruction: 为这个查询检索相关段落。 Query: ",
"hotpotqa": "Instruction: Given a multi-hop question, retrieve documents that can help answer the question. Query: ",
"quora": "Instruction: Given a question, retrieve questions that are semantically equivalent to the given question. Query: ",
"climate-fever": "Instruction: Given a claim about climate change, retrieve documents that support or refute the claim. Query: ",
"arguana": "Instruction: Given a claim, find documents that refute the claim. Query: ",
"fever": "Instruction: Given a claim, retrieve documents that support or refute the claim. Query: ",
"trec-covid": "Instruction: Given a query on COVID-19, retrieve documents that answer the query. Query: ",
"msmarco": "Instruction: Given a web search query, retrieve relevant passages that answer the query. Query: ",
"EcomRetrieval": "Instruction: 为这个查询检索相关商品标题。 Query: ",
"MedicalRetrieval": "Instruction: 为这个医学问题检索相关回答。 Query: ",
"CAQstack":"Instruction: Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question. Query: "
} |