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---
language:
- fa
tags:
- Wikipedia
- Summarizer
- bert2bert
- Summarization
task_categories:
- Summarization
- text generation
task_ids:
- news-articles-summarization
license:
- apache-2.0
multilinguality:
- monolingual
datasets:
- pn-summary
- XL-Sum
metrics:
- rouge-1
- rouge-2
- rouge-l
---
# WikiBert2WikiBert
Bert language models can be employed for Summarization tasks. WikiBert2WikiBert is an encoder-decoder transformer model that is initialized using the Persian WikiBert Model weights. The WikiBert Model is a Bert language model which is fine-tuned on Persian Wikipedia. After using the WikiBert weights for initialization, the model is trained for five epochs on PN-summary and Persian BBC datasets.
## How to Use:
You can use the code below to get the model's outputs, or you can simply use the demo on the right.
```
from transformers import (
BertTokenizerFast,
EncoderDecoderConfig,
EncoderDecoderModel,
BertConfig
)
model_name = 'Arashasg/WikiBert2WikiBert'
tokenizer = BertTokenizerFast.from_pretrained(model_name)
config = EncoderDecoderConfig.from_pretrained(model_name)
model = EncoderDecoderModel.from_pretrained(model_name, config=config)
def generate_summary(text):
inputs = tokenizer(text, padding="max_length", truncation=True, max_length=512, return_tensors="pt")
input_ids = inputs.input_ids.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
outputs = model.generate(input_ids, attention_mask=attention_mask)
output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)
return output_str
input = 'your input comes here'
summary = generate_summary(input)
```
## Evaluation
I separated 5 percent of the pn-summary for evaluation of the model. The rouge scores of the model are as follows:
| Rouge-1 | Rouge-2 | Rouge-l |
| ------------- | ------------- | ------------- |
| 38.97% | 18.42% | 34.50% |
|