Instructions to use google/tapas-medium-finetuned-tabfact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-medium-finetuned-tabfact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="google/tapas-medium-finetuned-tabfact")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("google/tapas-medium-finetuned-tabfact") model = AutoModelForSequenceClassification.from_pretrained("google/tapas-medium-finetuned-tabfact", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from google/tapas-medium-finetuned-tabfact: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/google/tapas-medium-finetuned-tabfact/resolve/main/tf_model.h5
- Command line
-
hf download hf://google/tapas-medium-finetuned-tabfact/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/google/tapas-medium-finetuned-tabfact/resolve/main/tf_model.h5
168 MB
- Xet hash:
- 35d31d4a05fcff3953c1b11942c1fa0d0f56f2555b76a7e69b4a6dd3ee53d1ac
- Size of remote file:
- 168 MB
- SHA256:
- 0aacd52addef02d6e06dffb5893fe3cba043e3ec657d87eb11e79225274abc1b
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