
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-ai-scibert](https://huggingface.co/morenolq/thext-ai-scibert) we performed an additional 
fine-tuning contextualizing the sentence with our custom context, namely PCE-best. The additional training epoch was performed on AIPubSumm (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 AIPubSumm with the following results:

|             | AIPubSumm |
|:-----------:|:---------:|
|  Rouge-1 F1 |   0.3415  |
|  Rouge-2 F1 |   0.1250  |
|  Rouge-L F1 |   0.3111  |
{"language": ["en"]}
transformers pytorch bert text-classification en autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 

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-ai-scibert we performed an additional
fine-tuning contextualizing the sentence with our custom context, namely PCE-best. The additional training epoch was performed on AIPubSumm (L. Cagliero,
M. La Quatra "Extracting highlights of scientific articles: A supervised summarization approach.").


You can find more details in the GitHub repo.


Usage
=====


Tis checkpoint should be loaded into 'BertForSequenceClassification.from\_pretrained'. See the BERT docs for more information.


Metrics
=======


We tested the model on AIPubSumm with the following results:




TAGS
#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #bert #text-classification #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
