Instructions to use Mozart-coder/BERT_spring-6_tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mozart-coder/BERT_spring-6_tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/BERT_spring-6_tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/BERT_spring-6_tokenized") model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/BERT_spring-6_tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e7ed4aeb5ff258b64bb27ebf0d1f3f8a27628cd0df0af595e44efccde111d99
- Size of remote file:
- 357 MB
- SHA256:
- ae75bf1442b8584f6639fb968b9d6936dade4d6452bdca8cb32d49b7a6fb1006
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