Instructions to use google/tapas-small-finetuned-wtq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-small-finetuned-wtq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="google/tapas-small-finetuned-wtq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("google/tapas-small-finetuned-wtq") model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-small-finetuned-wtq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/tapas-small-finetuned-wtq: direct link, hf CLI and curl.
- Browser
- Download file 117 MB
-
https://huggingface.co/google/tapas-small-finetuned-wtq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/tapas-small-finetuned-wtq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/tapas-small-finetuned-wtq/resolve/main/pytorch_model.bin
117 MB
- Xet hash:
- 7abc378c05b879b663043e97833574da9002c8ba36be9430cc35957052e731b7
- Size of remote file:
- 117 MB
- SHA256:
- c8686044453e815d0decec64256aeee6bba60f3ea8a1b722d736997e8d861209
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