Instructions to use hku-nlp/instructor-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hku-nlp/instructor-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hku-nlp/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 hku-nlp/instructor-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hku-nlp/instructor-base") model = AutoModel.from_pretrained("hku-nlp/instructor-base") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#3 opened about 2 years ago
by
SFconvertbot
__init__() got an unexpected keyword argument 'pooling_mode_weightedmean_tokens'
2
#2 opened over 3 years ago
by
p4rsimony
Duplicate repos?
👍 2
#1 opened over 3 years ago
by
nbroad