Instructions to use juliagsy/tapas-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juliagsy/tapas-fine-tuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="juliagsy/tapas-fine-tuned")# Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("juliagsy/tapas-fine-tuned") model = AutoModelForTableQuestionAnswering.from_pretrained("juliagsy/tapas-fine-tuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d6a1cdff26dfa05fc0d4e28ef944d0db0467cff5c4c33a9581b942dfdf13894
- Size of remote file:
- 1.35 GB
- SHA256:
- 3d7a0c9003016f9a94ef4ce3bf1091906821aa9f03bb2e412c40a5bc711a7b88
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