Instructions to use Mozart-coder/BERT_winter-6_tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mozart-coder/BERT_winter-6_tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/BERT_winter-6_tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/BERT_winter-6_tokenized") model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/BERT_winter-6_tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c38d8c98ba20b1aac911179ee23482a2e9302b874d571f8ff0c3c33785088633
- Size of remote file:
- 3.38 kB
- SHA256:
- af5152b90e563aedff2b9fc875929232ffbb0ab5266a0fbd24f2efea98c23450
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