Feature Extraction
Transformers
Safetensors
sentence-transformers
minicpm
image-feature-extraction
mteb
custom_code
Eval Results (legacy)
Instructions to use openbmb/MiniCPM-Embedding-Light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-Embedding-Light with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="openbmb/MiniCPM-Embedding-Light", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-Embedding-Light", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use openbmb/MiniCPM-Embedding-Light with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("openbmb/MiniCPM-Embedding-Light", 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: 989 Bytes
75f07f8 aafaf12 75f07f8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | import asyncio
from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine
import numpy as np
array = AsyncEngineArray.from_args([
EngineArgs(model_name_or_path = "openbmb/MiniCPM-Embedding-Light", engine="torch", dtype="float16", bettertransformer=False, pooling_method="mean", trust_remote_code=True),
])
queries = ["中国的首都是哪里?"] # "What is the capital of China?"
passages = ["beijing", "shanghai"] # "北京", "上海"
INSTRUCTION = "Query:"
queries = [f"{INSTRUCTION} {query}" for query in queries]
async def embed_text(engine: AsyncEmbeddingEngine,sentences):
async with engine:
embeddings, usage = await engine.embed(sentences=sentences)
return embeddings
queries_embedding = asyncio.run(embed_text(array[0],queries))
passages_embedding = asyncio.run(embed_text(array[0],passages))
scores = (np.array(queries_embedding) @ np.array(passages_embedding).T)
print(scores.tolist()) # [[0.40356746315956116, 0.36183443665504456]] |