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marksverdhei/unifiedqa-large-reddit-syac
unifiedqa-large-reddit-syac is a machine learning model from marksverdhei. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
This is an abstractive title answering (TA) / clickbait spoiling model. This is a variant of allenai/unifiedqa-t5-large, fine-tuned on the Reddit SYAC dataset. The model was trained as part of my masters thesis:
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From the Hugging Face model README
This is an abstractive title answering (TA) / clickbait spoiling model.
This is a variant of allenai/unifiedqa-t5-large, fine-tuned on the Reddit SYAC dataset.
The model was trained as part of my masters thesis:
Abstractive title answering for clickbait content
This model has the proven capability of generating, and hallucinating false information.
Any use of a TA system such as this one should be with knowledge of this risk.
The following scores is the result of intrinsic evaluation on the Reddit SYAC test set.
We used a max input length of 2048 and truncated the tokens exceeding this limit.
| rouge1 | rouge2 | rougeL | bleu | meteor |
|---|---|---|---|---|
| 44.58 | 23.89 | 43.45 | 17.46 | 36.22 |
Using human evaluation, we measured model performance by asking the evaluators to rate the models on a scale from 1 to 5 on how good their generated answer was for a given clickbait article.
Mean quality = 4.065
We included a factuality assessment to address the issue of generating false information.
Human raters were asked to place each output in the categories "True", "Irrelevant", and "False".
| True | Irrelevant | False |
|---|---|---|
| 85% | 7.5% | 7.5% |
If you use this model, please cite my master's thesis
@mastersthesis{heiervang2022AbstractiveTA
title={Abstractive title answering for clickbait content},
author={Markus Sverdvik Heiervang},
publisher={University of Oslo, Department of Informatics},
year={2022}
}