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/no_reset/pytorch_model.bin
- Command line
-
hf download hf://google/tapas-small-finetuned-sqa@no_reset/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google/tapas-small-finetuned-sqa/resolve/no_reset/pytorch_model.bin
117 MB
- Xet hash:
- 664ad2ed1f73be7831d24df281e6bbe04fe2e5498c4dc6b732a25b3ac1532e95
- Size of remote file:
- 117 MB
- SHA256:
- 8401411bb83648c2979404e29c91343bbbb9ae8d09894b24f554d79bd7aa483d
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