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-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hkunlp/instructor-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hkunlp/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 hkunlp/instructor-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hkunlp/instructor-large") model = AutoModel.from_pretrained("hkunlp/instructor-large") - Notebooks
- Google Colab
- Kaggle
sbert config is wrong
#28
by HarryCaveMan - opened
the sbert config states sentence-transformers version as 2.2.0 but that version does not support pooling_mode_weightedmean_tokens
The pip library InstructorEmbedding is abandonded and is no longer kept up to date. To use with the latest version of sentence-transformers (3.3.1), install this modified version:
pip install git+https://github.com/NoahBPeterson/instructor-embedding.git@54076ec450d9825cf84f1ed6e54a5748f6877070