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:
# pip install -U transformers accelerate # 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
Download .gitattributes from Pachinee/sentence2vec-brd: direct link, hf CLI and curl.
- Browser
- Download file 154 Bytes
-
https://huggingface.co/Pachinee/sentence2vec-brd/resolve/main/.gitattributes
- Command line
-
hf download hf://Pachinee/sentence2vec-brd/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/Pachinee/sentence2vec-brd/resolve/main/.gitattributes
154 Bytes
| model.safetensors filter=lfs diff=lfs merge=lfs -text | |
| tokenizer.json filter=lfs diff=lfs merge=lfs -text | |
| unigram.json filter=lfs diff=lfs merge=lfs -text | |