Feature Extraction
sentence-transformers
Safetensors
Transformers
English
modernbert
custom_code
text-embeddings-inference
Instructions to use infgrad/dewey_en_beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use infgrad/dewey_en_beta with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("infgrad/dewey_en_beta", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use infgrad/dewey_en_beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="infgrad/dewey_en_beta", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("infgrad/dewey_en_beta", trust_remote_code=True) model = AutoModel.from_pretrained("infgrad/dewey_en_beta", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Add pipeline & library name tags
#1
by tomaarsen HF Staff - opened
Hello!
Preface
This is very promising, wow. I'm looking forward to the report! Nice work integrating this with ST natively.
Pull Request overview
- Add
pipeline_tagas feature-extraction, i.e. embedding creation - Add
tagsfor sentence-transformers and transformers, so this model also shows up when filtering for those
Details
These tags should make it easier for this to be found.
- Tom Aarsen
Thank you very much!
infgrad changed pull request status to merged