Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
Chinese
qwen2
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use infly/inf-retriever-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use infly/inf-retriever-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("infly/inf-retriever-v1", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use infly/inf-retriever-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("infly/inf-retriever-v1", trust_remote_code=True) model = AutoModel.from_pretrained("infly/inf-retriever-v1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
请问这个模型和embedding模型的区别是什么呢?
#2
by ColinZhao - opened
我看到有retriever这个字样,和标准的embedding模型的作用是一样的吧?
我看到有retriever这个字样,和标准的embedding模型的作用是一样的吧?
这个模型是针对retrieval任务专门强化的,在retrieval任务上的用法和标准embedding模型一样,但是在其余的embedding任务上(例如classification、clustering等)效果可能会下降。
非常感谢您的回复!
ColinZhao changed discussion status to closed