Instructions to use Mozart-coder/BERT_Jan-6_tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mozart-coder/BERT_Jan-6_tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/BERT_Jan-6_tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/BERT_Jan-6_tokenized") model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/BERT_Jan-6_tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0cbedc159e7216058c54f35a00e27c3a56e91d55a826dcefc48f5f67fb85828
- Size of remote file:
- 357 MB
- SHA256:
- 542bba5858ca44bda5aaef50710a6834bfa0e8a202f1d0de4952e8990da3ef4d
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