Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use Stern5497/nir-xlm-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Stern5497/nir-xlm-roberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Stern5497/nir-xlm-roberta-base") 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 Stern5497/nir-xlm-roberta-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Stern5497/nir-xlm-roberta-base") model = AutoModel.from_pretrained("Stern5497/nir-xlm-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Stern5497/nir-xlm-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Stern5497/nir-xlm-roberta-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://Stern5497/nir-xlm-roberta-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Stern5497/nir-xlm-roberta-base/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 5472040f6d721c0f730c2b2502cd8b10ca5ca6ac6b4d3f104c0ad2776a9b6a7d
- Size of remote file:
- 17.1 MB
- SHA256:
- f1cc44ad7faaeec47241864835473fd5403f2da94673f3f764a77ebcb0a803ec
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