Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use nthakur/dragon-plus-query-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nthakur/dragon-plus-query-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nthakur/dragon-plus-query-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-query-encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nthakur/dragon-plus-query-encoder") model = AutoModel.from_pretrained("nthakur/dragon-plus-query-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56a5a4120b71b5fcccdf38697e72d3143ecdffe4d8079bcc9e28a44441b93cfb
- Size of remote file:
- 438 MB
- SHA256:
- 47945e25f2ce6e8c98aa170e87325f02ca2ec42de6cfa913ae8e076adee237d2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.