Sentence Similarity
sentence-transformers
Safetensors
French
xlm-roberta
legal
french
embeddings
retrieval
text-embeddings-inference
Instructions to use IvanDVonga/LegalEmbed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use IvanDVonga/LegalEmbed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("IvanDVonga/LegalEmbed") sentences = [ "C'est une personne heureuse", "C'est un chien heureux", "C'est une personne très heureuse", "Aujourd'hui est une journée ensoleillée" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 533 Bytes
e3c5a12 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"add_prefix_space": true,
"backend": "tokenizers",
"bos_token": "<s>",
"cls_token": "<s>",
"eos_token": "</s>",
"is_local": true,
"local_files_only": false,
"mask_token": "<mask>",
"max_length": 128,
"model_max_length": 512,
"pad_to_multiple_of": null,
"pad_token": "<pad>",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "</s>",
"stride": 0,
"tokenizer_class": "XLMRobertaTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "<unk>"
}
|