---
base_model: csebuetnlp/banglat5
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: banglat5-finetuned-headlineBT5_1000_WithIp_1
  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. -->

# banglat5-finetuned-headlineBT5_1000_WithIp_1

This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1889
- Rouge1 Precision: 0.192
- Rouge1 Recall: 0.1481
- Rouge1 Fmeasure: 0.1493
- Rouge2 Precision: 0.034
- Rouge2 Recall: 0.0238
- Rouge2 Fmeasure: 0.0257
- Rougel Precision: 0.1832
- Rougel Recall: 0.1382
- Rougel Fmeasure: 0.1402
- Rouge: {'rouge1_precision': 0.1920136634199134, 'rouge1_recall': 0.14811598124098124, 'rouge1_fmeasure': 0.14925985778926956, 'rouge2_precision': 0.03404265873015873, 'rouge2_recall': 0.023844246031746032, 'rouge2_fmeasure': 0.025712135087135088, 'rougeL_precision': 0.18318429834054833, 'rougeL_recall': 0.13817054473304474, 'rougeL_fmeasure': 0.14016822026013204}

## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rouge                                                                                                                                                                                                                                                                                                                                                                    |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 11.7469       | 1.0   | 160  | 8.0935          | 0.0715           | 0.1039        | 0.0761          | 0.0068           | 0.0122        | 0.0085          | 0.0715           | 0.1039        | 0.0761          | {'rouge1_precision': 0.07145305878761761, 'rouge1_recall': 0.10394435425685425, 'rouge1_fmeasure': 0.07614152865370223, 'rouge2_precision': 0.006805555555555556, 'rouge2_recall': 0.012217261904761904, 'rouge2_fmeasure': 0.008484477124183007, 'rougeL_precision': 0.07145305878761761, 'rougeL_recall': 0.10394435425685425, 'rougeL_fmeasure': 0.07614152865370223} |
| 8.8874        | 2.0   | 320  | 6.4819          | 0.1136           | 0.1427        | 0.1067          | 0.0217           | 0.0306        | 0.0217          | 0.1129           | 0.1406        | 0.1056          | {'rouge1_precision': 0.11364718738219125, 'rouge1_recall': 0.14271974553224553, 'rouge1_fmeasure': 0.10674004897414845, 'rouge2_precision': 0.02169890873015873, 'rouge2_recall': 0.030600198412698412, 'rouge2_fmeasure': 0.021724970898143597, 'rougeL_precision': 0.11286593738219125, 'rougeL_recall': 0.1406364121989122, 'rougeL_fmeasure': 0.10560368533778482}   |
| 7.5001        | 3.0   | 480  | 5.6537          | 0.1619           | 0.1529        | 0.1379          | 0.0297           | 0.0278        | 0.0251          | 0.1595           | 0.148         | 0.1347          | {'rouge1_precision': 0.16187199952824952, 'rouge1_recall': 0.15293786075036075, 'rouge1_fmeasure': 0.1378562003498065, 'rouge2_precision': 0.029678030303030303, 'rouge2_recall': 0.027787698412698413, 'rouge2_fmeasure': 0.02507508573298047, 'rougeL_precision': 0.15952157217782217, 'rougeL_recall': 0.14802714646464646, 'rougeL_fmeasure': 0.13468312342672956}   |
| 5.9849        | 4.0   | 640  | 5.2887          | 0.1799           | 0.1499        | 0.1427          | 0.0308           | 0.0238        | 0.0241          | 0.1714           | 0.14          | 0.1338          | {'rouge1_precision': 0.17989579864579863, 'rouge1_recall': 0.14991657647907647, 'rouge1_fmeasure': 0.14274962921924997, 'rouge2_precision': 0.030773809523809523, 'rouge2_recall': 0.023844246031746032, 'rouge2_fmeasure': 0.024054670819376702, 'rougeL_precision': 0.1713640526140526, 'rougeL_recall': 0.13997113997113997, 'rougeL_fmeasure': 0.13379535432747508}  |
| 6.7428        | 5.0   | 800  | 5.1889          | 0.192            | 0.1481        | 0.1493          | 0.034            | 0.0238        | 0.0257          | 0.1832           | 0.1382        | 0.1402          | {'rouge1_precision': 0.1920136634199134, 'rouge1_recall': 0.14811598124098124, 'rouge1_fmeasure': 0.14925985778926956, 'rouge2_precision': 0.03404265873015873, 'rouge2_recall': 0.023844246031746032, 'rouge2_fmeasure': 0.025712135087135088, 'rougeL_precision': 0.18318429834054833, 'rougeL_recall': 0.13817054473304474, 'rougeL_fmeasure': 0.14016822026013204}   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1