transformers pytorch bert text-classification en autotrain_compatible endpoints_compatible region:us
---
language:
- en
---

Model based on BERT, employed in a regression task to predict the Rouge-2 of a sentence with respect to the highlights of the paper. 
Starting from the model proposed with the paper [morenolq/thext-bio-scibert](https://huggingface.co/morenolq/thext-bio-scibert) we performed an additional 
fine-tuning contextualizing the sentence with our custom context, namely PCE-best. The additional training epoch was performed on BIOPubSumm (L. Cagliero, 
M. La Quatra "Extracting highlights of scientific articles: A supervised summarization approach.").

You can find more details in the [GitHub repo](https://github.com/nicolovergaro/DNLP_project).

# Usage
Tis checkpoint should be loaded into `BertForSequenceClassification.from_pretrained`. See the [BERT docs](https://huggingface.co/docs/transformers/v4.26.1/en/model_doc/bert#transformers.BertForSequenceClassification) for more information.

# Metrics
We tested the model on BIOPubSumm with the following results:

|             | BIOPubSumm |
|:-----------:|:---------:|
|  Rouge-1 F1 |   0.3335  |
|  Rouge-2 F1 |   0.1222  |
|  Rouge-L F1 |   0.3038  |