This is a version of the RoGPT2 model trained on the AlephNews dataset for the summarization task. There are 3 trained versions, they are available on the HuggingFace Hub:
Evaluation on AlephNews
Model | Decode Method | BERTScore | ROUGE | ||||
---|---|---|---|---|---|---|---|
Precision | Recall | F1-Score | ROUGE-1 | ROUGE-2 | ROUGE-L | ||
Greedy | 0.7335 | 0.7399 | 0.7358 | 0.3360 | 0.1862 | 0.3333 | |
Base | Beam Search | 0.7354 | 0.7468 | 0.7404 | 0.3480 | 0.1991 | 0.3416 |
Top-p Sampling | 0.7296 | 0.7299 | 0.7292 | 0.3058 | 0.1452 | 0.2951 | |
Greedy | 0.7378 | 0.7401 | 0.7380 | 0.3422 | 0.1922 | 0.3394 | |
Medium | Beam Search | 0.7390 | 0.7493 | 0.7434 | 0.3546 | 0.2061 | 0.3467 |
Top-p Sampling | 0.7315 | 0.7285 | 0.7294 | 0.3042 | 0.1400 | 0.2921 | |
Greedy | 0.7376 | 0.7424 | 0.7391 | 0.3414 | 0.1895 | 0.3355 | |
Large | Beam Search | 0.7394 | 0.7470 | 0.7424 | 0.3492 | 0.1995 | 0.3384 |
Top-p Sampling | 0.7311 | 0.7301 | 0.7299 | 0.3051 | 0.1418 | 0.2931 |
You can find a Jupyter Notebook where there is a demo for RoSummary and AlephNews.