Table Question Answering
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
multitabqa
multi-table-question-answering
Instructions to use vaishali/multitabqa-base-atis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaishali/multitabqa-base-atis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="vaishali/multitabqa-base-atis")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vaishali/multitabqa-base-atis") model = AutoModelForSeq2SeqLM.from_pretrained("vaishali/multitabqa-base-atis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa70aa1b03d8b6833a5aeafd34125d39afc01243e106315286197a4905dfb298
- Size of remote file:
- 558 MB
- SHA256:
- d5755c6be4b9752afb1bbc9255be9d889a11abad5fd7a40cf51cf19141489042
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