Table Question Answering
Transformers
PyTorch
English
bart
text2text-generation
table-fact-checking
table-to-text
Instructions to use Yale-LILY/reastap-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yale-LILY/reastap-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="Yale-LILY/reastap-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Yale-LILY/reastap-large") model = AutoModelForSeq2SeqLM.from_pretrained("Yale-LILY/reastap-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Yale-LILY/reastap-large: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/Yale-LILY/reastap-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Yale-LILY/reastap-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Yale-LILY/reastap-large/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- b7b0f33074330149aabcced5074d32377e1b2fcd2eb0a223898cbf8dcccf92a8
- Size of remote file:
- 1.63 GB
- SHA256:
- d980a6e7c8a8f0b71b6d9b378cb346d2363e905ee7e30c532f80700bdca85c41
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.