Instructions to use negfir/Bertbase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use negfir/Bertbase with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="negfir/Bertbase")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("negfir/Bertbase") model = AutoModelForMaskedLM.from_pretrained("negfir/Bertbase", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1ec10a8def59e8f96d77720e98e2943203c6031950916ca17605ca2799a9a8c
- Size of remote file:
- 433 MB
- SHA256:
- 7dbb57161cedfbe95ae5fe9caa58db429e7db0aae25b91dc927314a040b20b87
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