
A Bert2Bert model on VoA Persian Corpus (a medium-sized corpus of 7.9 million words, 2003-2008) generates headlines. The model achieved a 25.30 ROUGE-2 score. 
 
For more detail, please follow the [News Headline Generation](https://github.com/m3hrdadfi/news-headline-generation) repo. 


## Eval results
The following table summarizes the ROUGE scores obtained by the Bert2Bert model.

|    %    | Precision | Recall | FMeasure |
|:-------:|:---------:|:------:|:--------:|
| ROUGE-1 |   43.78   |  45.52 |   43.54  |
| ROUGE-2 |   24.50   | 25.30* |   24.24  |
| ROUGE-L |   41.20   |  42.22 |   40.76  |


## Questions?
Post a Github issue on the [News Headline Generation](https://github.com/hooshvare/news-headline-generation/issues) repo.

{"language": "fa", "license": "apache-2.0", "tags": ["summarization"]}
transformers pytorch encoder-decoder text2text-generation summarization fa license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

A Bert2Bert model on VoA Persian Corpus (a medium-sized corpus of 7.9 million words, 2003-2008) generates headlines. The model achieved a 25.30 ROUGE-2 score.


For more detail, please follow the News Headline Generation repo.


Eval results
------------


The following table summarizes the ROUGE scores obtained by the Bert2Bert model.



Questions?
----------


Post a Github issue on the News Headline Generation repo.



TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #summarization #fa #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
