
A Bert2Bert model on the Wiki Summary dataset to summarize articles. The model achieved an 8.47 ROUGE-2 score. 
 
For more detail, please follow the [Wiki Summary](https://github.com/m3hrdadfi/wiki-summary) repo. 


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

|    %    | Precision | Recall | FMeasure |
|:-------:|:---------:|:------:|:--------:|
| ROUGE-1 |   28.14   |  30.86 |   27.34  |
| ROUGE-2 |   07.12   | 08.47* |   07.10  |
| ROUGE-L |   28.49   |  25.87 |   25.50  |


## Questions?
Post a Github issue on the [Wiki Summary](https://github.com/m3hrdadfi/wiki-summary/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 the Wiki Summary dataset to summarize articles. The model achieved an 8.47 ROUGE-2 score.


For more detail, please follow the Wiki Summary repo.


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


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



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


Post a Github issue on the Wiki Summary 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 
