Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:404981
loss:MSELoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use vallabh001/xlm-roberta-base-multilingual-en-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vallabh001/xlm-roberta-base-multilingual-en-es with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vallabh001/xlm-roberta-base-multilingual-en-es") sentences = [ "It's not negative; it's positive.", "Las partes en conflicto también deben estar preparadas para volver a la mesa de negociación si se estanca la implementación del acuerdo.", "A veces refieren a él como al Campo de Prisioneros de Guerra Número 334, lugar donde viven ahora los lakota.", "No es negativo, es positivo." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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