Instructions to use BioMedTok/SentencePieceCharacters-NACHOS-FR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BioMedTok/SentencePieceCharacters-NACHOS-FR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BioMedTok/SentencePieceCharacters-NACHOS-FR")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BioMedTok/SentencePieceCharacters-NACHOS-FR") model = AutoModelForMaskedLM.from_pretrained("BioMedTok/SentencePieceCharacters-NACHOS-FR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 752 Bytes
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"additional_special_tokens": [
"<s>NOTUSED",
"</s>NOTUSED"
],
"bos_token": "<s>",
"clean_up_tokenization_spaces": true,
"cls_token": "<s>",
"eos_token": "</s>",
"mask_token": {
"__type": "AddedToken",
"content": "<mask>",
"lstrip": true,
"normalized": true,
"rstrip": false,
"single_word": false
},
"max_len": 512,
"model_max_length": 512,
"name_or_path": "./tokenizers/CharTokenizer_NACHOS_10M_lowercased_fixed_utf8/",
"pad_token": "<pad>",
"sep_token": "</s>",
"sp_model_kwargs": {},
"special_tokens_map_file": "./tokenizers/CharTokenizer_NACHOS_10M_lowercased_fixed_utf8/special_tokens_map.json",
"tokenizer_class": "CamembertTokenizer",
"unk_token": "<unk>",
"use_fast": true
}
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