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
- Xet hash:
- 7e03d60ff833f4d3385dbaefa75fa71f8cceb20338fdb297373fb3ccd993212b
- Size of remote file:
- 712 MB
- SHA256:
- 8c3cc504908107711395f5c747200dabb4987a4d6b358933145451c7694f17ed
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