Sentence Similarity
sentence-transformers
PyTorch
Transformers
Italian
bert
feature-extraction
text-embeddings-inference
Instructions to use danielivanov/embedding-model-it-mmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danielivanov/embedding-model-it-mmarco with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danielivanov/embedding-model-it-mmarco") 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 danielivanov/embedding-model-it-mmarco with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danielivanov/embedding-model-it-mmarco") model = AutoModel.from_pretrained("danielivanov/embedding-model-it-mmarco") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 630c30620f86d4395913a838a64397655afb7c8cc84d8a0a909c689e0083b82b
- Size of remote file:
- 440 MB
- SHA256:
- fc83d4c3685a7bfb81c185626f65d02980ee7f4fe894ab4b51416eb88c92445d
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