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
| { | |
| "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]" | |
| } | |