Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use nthakur/dragon-plus-context-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nthakur/dragon-plus-context-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nthakur/dragon-plus-context-encoder") 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 nthakur/dragon-plus-context-encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nthakur/dragon-plus-context-encoder") model = AutoModel.from_pretrained("nthakur/dragon-plus-context-encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 645ec9c3c1bf349b7f33b6b448cee0b843e8175220318aa5bf19351c492bcc99
- Size of remote file:
- 438 MB
- SHA256:
- ce9a1580e60d4e1f7a1a49b2aeb6138b0eaa9c9b766c1bb6fb7ad60e27f1be2c
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