---
license: mit
language:
- ne
metrics:
- rouge
pipeline_tag: text-generation
tags:
- Nepali summary
- Nepali bart
- Nepali
- summary
---
# Nep_Summ_BART:

<!-- Provide a quick summary of what the model is/does. -->

This model is pre-trained using BART on Nepali corpus and then fine-tuned on Nepali summary data.
<br>The model generates a summary for the text input.

## Model Details

### Model Description

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

The model is trained using BART noising techniques like sentence permutation, token deletion, and random token masking.
<br>The noisy data is fed into the encoder of the transformer and the denoising task/ objective is fulfilled by the decoder of the transformer model.

Normal cross-entropy loss is used for both the pre-training and fine-tuning of the model.

The Loss for pre-training is as follows:

| Epoch   |      Training Loss      |  Val Loss |
|----------|:-------------:|------:|
| 1 |  0.8137 | 0.8010 |
| 2 |  0.7861 | 0.7524 |
| 3 |  0.7495 | 0.7290 |

The ROUGE Score for the fine-tuning using the BBC XLSum Nepali Test Dataset is:

ROUGE1 : 0.177
ROUGE2 : 0.059
ROUGEL : 0.154

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
You can use this model for text summarization.
### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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