Instructions to use google/tapas-tiny-finetuned-wtq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-tiny-finetuned-wtq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="google/tapas-tiny-finetuned-wtq")# Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("google/tapas-tiny-finetuned-wtq") model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-tiny-finetuned-wtq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/tapas-tiny-finetuned-wtq: direct link, hf CLI and curl.
- Browser
- Download file 18.1 MB
-
https://huggingface.co/google/tapas-tiny-finetuned-wtq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/tapas-tiny-finetuned-wtq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/tapas-tiny-finetuned-wtq/resolve/main/pytorch_model.bin
18.1 MB
- Xet hash:
- 95e3b1f5ff0bf09372f931bb4220b305a2e10adb148b93a4432b3392993ca97f
- Size of remote file:
- 18.1 MB
- SHA256:
- 86ac6d2613bae2898f15dc7eca67b0e638427a47bbb519fa02bcf54d20b196ea
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