Instructions to use Mozart-coder/BERT_newdata-6_tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mozart-coder/BERT_newdata-6_tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mozart-coder/BERT_newdata-6_tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mozart-coder/BERT_newdata-6_tokenized") model = AutoModelForMaskedLM.from_pretrained("Mozart-coder/BERT_newdata-6_tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c48ebe2c77ab461c098c825608526ad2e1ae4ab083caaa79f81f694b71a29df8
- Size of remote file:
- 357 MB
- SHA256:
- f5bf82ddd4973992a20ec28c97e5d1f89eb06371006bb0eac85d6d85f2fbf9ff
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