Sentence Similarity
sentence-transformers
PyTorch
roberta
feature-extraction
text-embeddings-inference
Instructions to use tanvirsrbd1/distilroberta-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tanvirsrbd1/distilroberta-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tanvirsrbd1/distilroberta-v2") 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] - Notebooks
- Google Colab
- Kaggle
Download added_tokens.json from tanvirsrbd1/distilroberta-v2: direct link, hf CLI and curl.
- Browser
- Download file 75 Bytes
-
https://huggingface.co/tanvirsrbd1/distilroberta-v2/resolve/0a48e22a3351ac1efbb0299f48c4d33e9c66003d/added_tokens.json
- Command line
-
hf download hf://tanvirsrbd1/distilroberta-v2@0a48e22a3351ac1efbb0299f48c4d33e9c66003d/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/tanvirsrbd1/distilroberta-v2/resolve/0a48e22a3351ac1efbb0299f48c4d33e9c66003d/added_tokens.json
75 Bytes
| { | |
| "</s>": 2, | |
| "<mask>": 50264, | |
| "<pad>": 1, | |
| "<s>": 0, | |
| "<unk>": 3 | |
| } | |