Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use Pachinee/sentence2vec-brd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Pachinee/sentence2vec-brd with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Pachinee/sentence2vec-brd") 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 Pachinee/sentence2vec-brd with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Pachinee/sentence2vec-brd") model = AutoModel.from_pretrained("Pachinee/sentence2vec-brd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08b98304697e963bdc3ad9057801eae569b73e73cd68339bc244902b31f506f9
- Size of remote file:
- 17.1 MB
- SHA256:
- fa685fc160bbdbab64058d4fc91b60e62d207e8dc60b9af5c002c5ab946ded00
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