Sentence Similarity
Safetensors
sentence-transformers
Italian
pylate
modernbert
colbert
late-interaction
italian
retrieval
information-retrieval
rag
multi-vector
text-embeddings-inference
Instructions to use enricollen/ItColBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use enricollen/ItColBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("enricollen/ItColBERT") 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] - Notebooks
- Google Colab
- Kaggle
File size: 700 Bytes
6bd0ed7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"is_local": true,
"mask_token": "[MASK]",
"max_length": 8192,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 511,
"pad_to_multiple_of": null,
"pad_token": "[MASK]",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "[SEP]",
"special_tokens": {
"cls_token": "[CLS]",
"mask_token": "[MASK]",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"unk_token": "[UNK]"
},
"stride": 0,
"tokenizer_class": "TokenizersBackend",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
|