---
base_model: google/mt5-base
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mt5-base_Nepali_News_Summarization_0
  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. -->

# mt5-base_Nepali_News_Summarization_0

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3685
- Rouge-1 R: 0.3321
- Rouge-1 P: 0.3218
- Rouge-1 F: 0.3186
- Rouge-2 R: 0.1761
- Rouge-2 P: 0.1703
- Rouge-2 F: 0.1677
- Rouge-l R: 0.3234
- Rouge-l P: 0.3133
- Rouge-l F: 0.3102
- Gen Len: 15.7133

## 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

### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------:|
| 1.8844        | 1.0   | 10191 | 1.4867          | 0.31      | 0.3133    | 0.3024    | 0.1576    | 0.1605    | 0.1531    | 0.3015    | 0.3048    | 0.2942    | 15.2667 |
| 1.7381        | 2.0   | 20382 | 1.4401          | 0.3203    | 0.3104    | 0.3068    | 0.1675    | 0.162     | 0.1592    | 0.3121    | 0.3026    | 0.299     | 15.699  |
| 1.6401        | 3.0   | 30573 | 1.3685          | 0.3321    | 0.3218    | 0.3186    | 0.1761    | 0.1703    | 0.1677    | 0.3234    | 0.3133    | 0.3102    | 15.7133 |


### Framework versions

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