Instructions to use google/tapas-mini-masklm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-mini-masklm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google/tapas-mini-masklm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google/tapas-mini-masklm") model = AutoModelForMaskedLM.from_pretrained("google/tapas-mini-masklm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba5982ad8bc1fe9028be919a7ce63bf79fa645094c1f199eb7b171bcab0f23a1
- Size of remote file:
- 45.9 MB
- SHA256:
- 4a19e36504a481d1cd088d850b385dbfc94a177717cab9345fb8dd414007b493
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