Instructions to use ARTeLab/it5-summarization-fanpage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARTeLab/it5-summarization-fanpage with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="ARTeLab/it5-summarization-fanpage")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ARTeLab/it5-summarization-fanpage") model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/it5-summarization-fanpage") - Notebooks
- Google Colab
- Kaggle
Add evaluation results on the ARTeLab--fanpage config and test split of ARTeLab/fanpage
Beep boop, I am a bot from Hugging Face's automatic model evaluator π!
Your model has been evaluated on the ARTeLab--fanpage config and test split of the ARTeLab/fanpage dataset by @morenolq , using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.
Out of curiosity, I evaluated the model using the automatic evaluator. However, the results reported by the evaluator ARE NOT correct! Running the test on my own machine I got similar results wrt. what was reported by the authors:
ROUGE-1: 0.3398450558489963
ROUGE-2: 0.15581617114835214
ROUGE-L: 0.2489955973458257
ROUGE-Lsum: 0.24907289562535354
BERTScore P: 0.7061309858324744
BERTScore R: 0.7007822649193782
BERTScore F1: 0.7030036912945559
Thanks!
Moreno