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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |