Instructions to use google/tapas-base-masklm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-base-masklm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google/tapas-base-masklm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google/tapas-base-masklm") model = AutoModelForMaskedLM.from_pretrained("google/tapas-base-masklm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86b636a29b30efa3ef4e46022b3cded3b157d4eca899db17ef66e7fbb7e60825
- Size of remote file:
- 443 MB
- SHA256:
- 10ad853569963b50261ac55589458639e905f76a0c86c2858dc5d484d8e0301c
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