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-xl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hkunlp/instructor-xl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hkunlp/instructor-xl") 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-xl with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hkunlp/instructor-xl") model = AutoModel.from_pretrained("hkunlp/instructor-xl") - Notebooks
- Google Colab
- Kaggle
Are these objects thread safe for concurrent use?
#21
by hiranya911 - opened
Can I use the same INSTRUCTOR object from multiple threads for encoding? Any issues we need to be aware of?
Hi, Thanks a lot for your interest in the INSTRUCTOR model!
Yes, it is possible to use INSTRUCTOR in multi-threading process. You may need to take care of the model/data copy/parallel issues in the encoding.