---
datasets:
- mbshr/XSUMUrdu-DW_BBC
language:
- ur
metrics:
- rouge
- bertscore
pipeline_tag: summarization
---
# Model Card for Model ID

### Summarization Model (Type:T5)

Summarization: Extractive and Abstractive
- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.
  - Fine-tuned on https://huggingface.co/mbshr/XSUMUrdu-DW_BBC, ref to https://doi.org/10.48550/arXiv.2310.02790 for details.


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** urT5 adapted version of mT5
- **Language(s) (NLP):** Urdu
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** google/mt5-base

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** https://doi.org/10.48550/arXiv.2310.02790

## Uses

Summarization

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details


## Evaluation & Results

<!-- This section describes the evaluation protocols and provides the results. -->

Evaluated on https://huggingface.co/mbshr/XSUMUrdu-DW_BBC
- ROUGE-1 F Score: 40.03 combined, 46.35 BBC Urdu datapoints only and 36.91 DW Urdu datapoints only)
- BERTScore: 75.1 combined, 77.0 BBC Urdu datapoints only and 74.16 DW Urdu datapoints only

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

@misc{munaf2023low,
    title={Low Resource Summarization using Pre-trained Language Models},
    author={Mubashir Munaf and Hammad Afzal and Naima Iltaf and Khawir Mahmood},
    year={2023},
    eprint={2310.02790},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

## Contact

- mubashir.munaaf@gmail.com