Table Question Answering
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
multitabqa
multi-table-question-answering
Instructions to use vaishali/multitabqa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaishali/multitabqa-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="vaishali/multitabqa-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vaishali/multitabqa-base") model = AutoModelForSeq2SeqLM.from_pretrained("vaishali/multitabqa-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d947a5f6286729d8949c84a33b4cc9d3ac3ad711055267d39d4fb22778c2be76
- Size of remote file:
- 558 MB
- SHA256:
- 55816c9761483791e804f56fbe6f20b926f3cfef9e023561410fec5de4b384c1
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