Instructions to use google/tapas-small-finetuned-tabfact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-small-finetuned-tabfact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="google/tapas-small-finetuned-tabfact")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("google/tapas-small-finetuned-tabfact") model = AutoModelForSequenceClassification.from_pretrained("google/tapas-small-finetuned-tabfact") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3dc457297a523a331cc8ed84289999aac86531a33881de959e41211c2eac9ba9
- Size of remote file:
- 117 MB
- SHA256:
- 65e67b379706eb0bcf191a7f61fd5c6a666b114bc6bad8652ef1af401ac569aa
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