---
base_model: facebook/mbart-large-50
library_name: peft
license: mit
tags:
- generated_from_trainer
model-index:
- name: mbart-large-50_Nepali_News_Summarization_QLoRA_8bit
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# mbart-large-50_Nepali_News_Summarization_QLoRA_8bit

This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3724
- Rouge-1 R: 0.3809
- Rouge-1 P: 0.3877
- Rouge-1 F: 0.3745
- Rouge-2 R: 0.2144
- Rouge-2 P: 0.2176
- Rouge-2 F: 0.2093
- Rouge-l R: 0.3702
- Rouge-l P: 0.3766
- Rouge-l F: 0.364
- Gen Len: 14.0747

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge-1 R | Rouge-1 P | Rouge-1 F | Rouge-2 R | Rouge-2 P | Rouge-2 F | Rouge-l R | Rouge-l P | Rouge-l F | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------:|
| 1.5604        | 1.0   | 10191 | 1.5916          | 0.3605    | 0.3694    | 0.3536    | 0.1948    | 0.2008    | 0.19      | 0.3501    | 0.3586    | 0.3433    | 14.7262 |
| 1.5482        | 2.0   | 20382 | 1.3992          | 0.3673    | 0.3879    | 0.3672    | 0.2034    | 0.2149    | 0.202     | 0.3577    | 0.3775    | 0.3575    | 13.7928 |
| 1.2397        | 3.0   | 30573 | 1.3724          | 0.3809    | 0.3877    | 0.3745    | 0.2144    | 0.2176    | 0.2093    | 0.3702    | 0.3766    | 0.364     | 14.0747 |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1