Sentence Similarity
sentence-transformers
PyTorch
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-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 projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base") model = AutoModel.from_pretrained("projecte-aina/ST-NLI-ca_paraphrase-multilingual-mpnet-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 355fc5cf46b6f55e685914e50e47fb206e5930fe2266b022344575b8e1e88551
- Size of remote file:
- 1.11 GB
- SHA256:
- 5ccd79a44ad1889ba22714efdc6893a40a62708cc65c50f0e049d5862b448733
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