Instructions to use lizhuang144/flan-t5-base-VG-factual-sg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lizhuang144/flan-t5-base-VG-factual-sg with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lizhuang144/flan-t5-base-VG-factual-sg") model = AutoModelForSeq2SeqLM.from_pretrained("lizhuang144/flan-t5-base-VG-factual-sg") - Notebooks
- Google Colab
- Kaggle
This is a flan-t5-based model pre-trained on VG scene graph parsing dataset first and then fine-tuned on FACTUAL scene graph parsing dataset. See model details from 'https://github.com/zhuang-li/FACTUAL/tree/main '.
If you use the model, please cite:
@inproceedings{li-etal-2023-factual,
title = "{FACTUAL}: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing",
author = "Li, Zhuang and
Chai, Yuyang and
Zhuo, Terry Yue and
Qu, Lizhen and
Haffari, Gholamreza and
Li, Fei and
Ji, Donghong and
Tran, Quan Hung",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.398",
pages = "6377--6390",
}
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