Sentence Similarity
sentence-transformers
PyTorch
ONNX
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 vectoriseai/instructor-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vectoriseai/instructor-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vectoriseai/instructor-large") 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 vectoriseai/instructor-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vectoriseai/instructor-large") model = AutoModel.from_pretrained("vectoriseai/instructor-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0836d05476fd95acaa891626776a2fb5680d84b4a52aee6bf23dbb2c0a4552fa
- Size of remote file:
- 337 MB
- SHA256:
- dd153056a2f8579a1bff184c1f52546970b7e493e2cf1ed7d8a92de3a7a06bde
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