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
Update model card: Add paper link, abstract, and library name
#2
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the link to the paper next to the title.
- Adding the abstract of the paper to the model card.
- Adding
library_name: sentence-transformersto the metadata.
infgrad changed pull request status to merged