Instructions to use google/tapas-small-masklm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-small-masklm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google/tapas-small-masklm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google/tapas-small-masklm") model = AutoModelForMaskedLM.from_pretrained("google/tapas-small-masklm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 310d6382216219bc7db5ed06396e50deef7e5a607eff54e34935c71e4a23b2b6
- Size of remote file:
- 117 MB
- SHA256:
- 26273ad4c02f16a5d98db48ba248684242df159f91e5f1c6e47cb8ae2d653754
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