---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
model-index:
- name: bengali_news_article_summarization_mt5
  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. -->

# bengali_news_article_summarization_mt5

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2111

## 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: 0.001
- train_batch_size: 20
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 160
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 100
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.99  | 83   | 0.8963          |
| No log        | 2.0   | 167  | 0.3201          |
| 9.149         | 2.99  | 250  | 0.2583          |
| 9.149         | 3.99  | 334  | 0.2372          |
| 0.3009        | 5.0   | 418  | 0.2298          |
| 0.3009        | 5.99  | 501  | 0.2244          |
| 0.3009        | 7.0   | 585  | 0.2213          |
| 0.2524        | 8.0   | 669  | 0.2163          |
| 0.2524        | 8.99  | 752  | 0.2136          |
| 0.2306        | 10.0  | 836  | 0.2126          |
| 0.2306        | 10.99 | 919  | 0.2117          |
| 0.2176        | 11.99 | 1003 | 0.2120          |
| 0.2176        | 13.0  | 1087 | 0.2116          |
| 0.2176        | 13.99 | 1170 | 0.2111          |
| 0.2119        | 14.89 | 1245 | 0.2111          |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2