README.md

metadata

base_model: csebuetnlp/banglat5
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: banglat5-finetuned-headlineBT5_1000_WithIp_1
    results: []

banglat5-finetuned-headlineBT5_1000_WithIp_1

This model is a fine-tuned version of 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