Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
xlm-roberta
feature-extraction
zero-shot-classification
text-embeddings-inference
Instructions to use symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli") sentences = [ "هذا شخص سعيد", "هذا كلب سعيد", "هذا شخص سعيد جدا", "اليوم هو يوم مشمس" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli") model = AutoModelForMultimodalLM.from_pretrained("symanto/sn-xlm-roberta-base-snli-mnli-anli-xnli") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b03b9e079abc849bdd27d0942fa6a77f9e7836db188512be97e4b3d52f415a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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