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--- |
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base_model: csebuetnlp/banglat5 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: banglat5-finetuned-headlineBT5_1000_WithIp_1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# banglat5-finetuned-headlineBT5_1000_WithIp_1 |
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This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 5.1889 |
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- Rouge1 Precision: 0.192 |
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- Rouge1 Recall: 0.1481 |
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- Rouge1 Fmeasure: 0.1493 |
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- Rouge2 Precision: 0.034 |
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- Rouge2 Recall: 0.0238 |
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- Rouge2 Fmeasure: 0.0257 |
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- Rougel Precision: 0.1832 |
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- Rougel Recall: 0.1382 |
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- Rougel Fmeasure: 0.1402 |
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- 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} |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 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} | |
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| 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} | |
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| 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} | |
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| 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} | |
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| 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} | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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