Instructions to use google/tapas-large-finetuned-tabfact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-large-finetuned-tabfact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="google/tapas-large-finetuned-tabfact")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-tabfact") model = AutoModelForSequenceClassification.from_pretrained("google/tapas-large-finetuned-tabfact", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 458a65563406fcc362cff7062cf719a60e3d3dda46e7ca90027e40144553d23c
- Size of remote file:
- 1.35 GB
- SHA256:
- 2c52710ad14b548c74bd8a44fc5d0761f799035b215eae57e41700e2c20a9f1c
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