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:
- 31a45a0bbd732ee7a4c224789991324727930f8846c28a3881e34eafce60df5c
- Size of remote file:
- 3.38 kB
- SHA256:
- 34f85666894e872e39c04d33f99c6bf18e25d5f13ff5c881145fab59f19d9eaa
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