Sentence Similarity
sentence-transformers
Safetensors
Transformers
Italian
English
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use nickprock/xlmr-ted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nickprock/xlmr-ted with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nickprock/xlmr-ted") sentences = [ "Questa è una persona felice", "Questo è un cane felice", "Questa è una persona molto felice", "Oggi è una giornata di sole" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use nickprock/xlmr-ted with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nickprock/xlmr-ted") model = AutoModel.from_pretrained("nickprock/xlmr-ted") - Notebooks
- Google Colab
- Kaggle
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