Instructions to use google/tapas-small-finetuned-sqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tapas-small-finetuned-sqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="google/tapas-small-finetuned-sqa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("google/tapas-small-finetuned-sqa") model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-small-finetuned-sqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from google/tapas-small-finetuned-sqa: direct link, hf CLI and curl.
- Browser
- Download file 117 MB
-
https://huggingface.co/google/tapas-small-finetuned-sqa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google/tapas-small-finetuned-sqa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/tapas-small-finetuned-sqa/resolve/main/pytorch_model.bin
117 MB
- Xet hash:
- ca600f3c5bc542fad1c7fe36a02998cf853eedd28016198a63f51048a5503ae6
- Size of remote file:
- 117 MB
- SHA256:
- 46da47690c94335c26dd575b4ad3de8a3ce9dcc498507315609d0c18ae9058c3
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