Instructions to use Mozart-coder/BERT_full_data3-6_tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mozart-coder/BERT_full_data3-6_tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/BERT_full_data3-6_tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/BERT_full_data3-6_tokenized") model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/BERT_full_data3-6_tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad0c5243175a37916749e1a19c0c48757f52dd1f5f4d69176eafb58bc5644b8d
- Size of remote file:
- 357 MB
- SHA256:
- e264192e463a255386c7ea9b64f1f271d8ef21f68a0a6720cd9bbd193cfadca8
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