Instructions to use MLRS/BERTu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLRS/BERTu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="MLRS/BERTu")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("MLRS/BERTu") model = AutoModelForMaskedLM.from_pretrained("MLRS/BERTu") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8dcfc3d15677fc7dd3fcb668e4b54dae6d17b6e8fb4ae35e69d2565cb15f2554
- Size of remote file:
- 504 MB
- SHA256:
- 1f3baf8cf5ae15c737414b8c7aecbeb48542e9056b036abf556e6a19c3ef794e
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