Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
t5
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
prompt-retrieval
text-reranking
feature-extraction
English
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
Eval Results (legacy)
Instructions to use hkunlp/instructor-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hkunlp/instructor-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hkunlp/instructor-base") 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 hkunlp/instructor-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hkunlp/instructor-base") model = AutoModel.from_pretrained("hkunlp/instructor-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c526a2435ea3f0b66071868618af1dbf4a316c2b1dee91f56b411ee05e88fab6
- Size of remote file:
- 439 MB
- SHA256:
- 5ee57a1fca2d2d2fad7dd837e17d65b66bb86198d2f89b514982d0cf7c30fa80
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