Sentence Similarity
sentence-transformers
Safetensors
Transformers
Vietnamese
roberta
feature-extraction
text-embeddings-inference
Instructions to use thehosy/vi-roberta-base-qa-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thehosy/vi-roberta-base-qa-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thehosy/vi-roberta-base-qa-embedding") 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 thehosy/vi-roberta-base-qa-embedding with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("thehosy/vi-roberta-base-qa-embedding") model = AutoModel.from_pretrained("thehosy/vi-roberta-base-qa-embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from thehosy/vi-roberta-base-qa-embedding: direct link, hf CLI and curl.
- Browser
- Download file 2.22 MB
-
https://huggingface.co/thehosy/vi-roberta-base-qa-embedding/resolve/main/tokenizer.json
- Command line
-
hf download hf://thehosy/vi-roberta-base-qa-embedding/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/thehosy/vi-roberta-base-qa-embedding/resolve/main/tokenizer.json
2.22 MB
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