Table Question Answering
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
multitabqa
multi-table-question-answering
Instructions to use vaishali/multitabqa-base-geoquery with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaishali/multitabqa-base-geoquery with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="vaishali/multitabqa-base-geoquery")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vaishali/multitabqa-base-geoquery") model = AutoModelForSeq2SeqLM.from_pretrained("vaishali/multitabqa-base-geoquery", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vaishali/multitabqa-base-geoquery: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/vaishali/multitabqa-base-geoquery/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://vaishali/multitabqa-base-geoquery@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vaishali/multitabqa-base-geoquery/resolve/refs%2Fpr%2F1/pytorch_model.bin
558 MB
- Xet hash:
- e12ef3ade1186046a6e7c88b1fbc8738b109117a842b245e01cbf5404af843d6
- Size of remote file:
- 558 MB
- SHA256:
- e5f4ac49ceb2acacbb2cfe169ea252b46cf770f35fe9aa5030eb84504bdbe824
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