Multi-dimensional evaluation
is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimensions, such as coherence and fluency.
However, automatic evaluation in NLG is still dominated by similarity-based metrics (e.g., ROUGE, BLEU), but they are not sufficient to portray the difference between the advanced generation models.
Therefore, we propose
UniEval
to bridge this gap so that a more comprehensive and fine-grained evaluation of NLG systems can be achieved.
Pre-trained Evaluator
unieval-fact
is the pre-trained evaluator for the factual consistency detection task. It can evaluate the model output and predict a consistency score.
More Information About unieval-fact huggingface.co Model
unieval-fact huggingface.co
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MingZhong unieval-fact online free url in huggingface.co:
unieval-fact is an open source model from GitHub that offers a free installation service, and any user can find unieval-fact on GitHub to install. At the same time, huggingface.co provides the effect of unieval-fact install, users can directly use unieval-fact installed effect in huggingface.co for debugging and trial. It also supports api for free installation.