Instructions to use google/tapas-large-masklm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-large-masklm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google/tapas-large-masklm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-masklm") model = AutoModelForMaskedLM.from_pretrained("google/tapas-large-masklm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31d2e4e14d15eb096f23a55d60752d2359a0ebd4bb3dc3235469a94822d78387
- Size of remote file:
- 1.35 GB
- SHA256:
- a992fa6081ab1a8116dc19603c0497569d06e411554b12ed9096811902379c58
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