Instructions to use billfass/multilingual_bert_model_classiffication with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use billfass/multilingual_bert_model_classiffication with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="billfass/multilingual_bert_model_classiffication")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("billfass/multilingual_bert_model_classiffication") model = AutoModelForMaskedLM.from_pretrained("billfass/multilingual_bert_model_classiffication", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 315 Bytes
bfe9063 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_lower_case": false,
"mask_token": "[MASK]",
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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