Table Question Answering
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
multitabqa
multi-table-question-answering
Instructions to use vaishali/multitabqa-base-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaishali/multitabqa-base-sql with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="vaishali/multitabqa-base-sql")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vaishali/multitabqa-base-sql") model = AutoModelForSeq2SeqLM.from_pretrained("vaishali/multitabqa-base-sql") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c55054f9ce2b634a007ce93278d804aa4d6d7d3479241ab914d2d3dc2ccfde59
- Size of remote file:
- 558 MB
- SHA256:
- 791d1876f837d83963c7c00586d835b1f979165fcc9cd012dca9d7606a26b083
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