---
library_name: transformers
base_model: csebuetnlp/banglabert
tags:
- generated_from_trainer
model-index:
- name: bengali_qa_model_AGGRO_banglabert
  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_qa_model_AGGRO_banglabert

This model is a fine-tuned version of [csebuetnlp/banglabert](https://huggingface.co/csebuetnlp/banglabert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2676
- Exact Match: 98.5714
- F1 Score: 99.0056

## 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: 4
- eval_batch_size: 4
- seed: 3407
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 100

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Exact Match | F1 Score |
|:-------------:|:------:|:----:|:---------------:|:-----------:|:--------:|
| 6.0126        | 0.0053 | 1    | 5.9783          | 0.0         | 0.6103   |
| 6.0125        | 0.0107 | 2    | 5.9540          | 0.0         | 0.7848   |
| 5.9675        | 0.0160 | 3    | 5.9074          | 0.0         | 0.9597   |
| 5.9287        | 0.0214 | 4    | 5.8425          | 0.0         | 1.7507   |
| 5.8586        | 0.0267 | 5    | 5.7636          | 0.1504      | 4.2535   |
| 5.8206        | 0.0321 | 6    | 5.6740          | 0.4511      | 11.2628  |
| 5.7246        | 0.0374 | 7    | 5.5749          | 1.7293      | 23.3816  |
| 5.634         | 0.0428 | 8    | 5.4574          | 3.9850      | 37.7873  |
| 5.4963        | 0.0481 | 9    | 5.3105          | 5.7895      | 47.4987  |
| 5.2985        | 0.0535 | 10   | 5.1265          | 7.5940      | 52.0471  |
| 5.182         | 0.0588 | 11   | 4.8997          | 11.9549     | 54.5555  |
| 4.973         | 0.0641 | 12   | 4.6631          | 15.9398     | 56.5530  |
| 4.8353        | 0.0695 | 13   | 4.4348          | 19.6241     | 58.6313  |
| 4.6269        | 0.0748 | 14   | 4.2322          | 23.5338     | 60.7029  |
| 4.4238        | 0.0802 | 15   | 4.0467          | 28.0451     | 62.7494  |
| 4.1976        | 0.0855 | 16   | 3.8781          | 32.7068     | 64.6375  |
| 4.1302        | 0.0909 | 17   | 3.7200          | 35.7895     | 66.2513  |
| 3.9139        | 0.0962 | 18   | 3.5621          | 39.5489     | 67.7758  |
| 3.8521        | 0.1016 | 19   | 3.4019          | 43.0075     | 69.2899  |
| 3.7003        | 0.1069 | 20   | 3.2534          | 46.0150     | 70.6373  |
| 3.5972        | 0.1123 | 21   | 3.1168          | 48.8722     | 72.3043  |
| 3.5249        | 0.1176 | 22   | 2.9875          | 51.5038     | 73.2903  |
| 3.1756        | 0.1230 | 23   | 2.8600          | 53.6090     | 74.1609  |
| 3.2323        | 0.1283 | 24   | 2.7356          | 55.2632     | 74.8864  |
| 3.0696        | 0.1336 | 25   | 2.6150          | 56.8421     | 75.8938  |
| 2.9806        | 0.1390 | 26   | 2.5029          | 58.9474     | 77.3831  |
| 2.8261        | 0.1443 | 27   | 2.3997          | 61.1278     | 78.8467  |
| 2.8965        | 0.1497 | 28   | 2.3045          | 63.9098     | 80.8890  |
| 2.6622        | 0.1550 | 29   | 2.2151          | 66.0902     | 82.4263  |
| 2.5132        | 0.1604 | 30   | 2.1300          | 68.0451     | 83.8984  |
| 2.5076        | 0.1657 | 31   | 2.0482          | 70.9774     | 85.4846  |
| 2.2189        | 0.1711 | 32   | 1.9678          | 72.7068     | 86.2628  |
| 2.0851        | 0.1764 | 33   | 1.8883          | 75.8647     | 87.8992  |
| 2.1198        | 0.1818 | 34   | 1.8091          | 78.5714     | 89.4148  |
| 2.0272        | 0.1871 | 35   | 1.7300          | 80.8271     | 90.3877  |
| 1.9951        | 0.1924 | 36   | 1.6514          | 82.7068     | 91.2138  |
| 1.7741        | 0.1978 | 37   | 1.5736          | 84.9624     | 91.8920  |
| 1.9176        | 0.2031 | 38   | 1.4970          | 86.5414     | 92.3250  |
| 1.8599        | 0.2085 | 39   | 1.4219          | 87.5940     | 92.7578  |
| 1.8095        | 0.2138 | 40   | 1.3496          | 88.5714     | 93.0980  |
| 1.7814        | 0.2192 | 41   | 1.2790          | 90.0752     | 93.7737  |
| 1.4602        | 0.2245 | 42   | 1.2103          | 91.5038     | 94.6447  |
| 1.5147        | 0.2299 | 43   | 1.1431          | 92.1805     | 95.1039  |
| 1.4205        | 0.2352 | 44   | 1.0774          | 92.9323     | 95.4111  |
| 1.3222        | 0.2406 | 45   | 1.0127          | 93.9850     | 96.0199  |
| 1.2477        | 0.2459 | 46   | 0.9508          | 94.8120     | 96.5219  |
| 1.1406        | 0.2513 | 47   | 0.8936          | 95.2632     | 96.8391  |
| 1.1698        | 0.2566 | 48   | 0.8382          | 96.3158     | 97.5331  |
| 1.1359        | 0.2619 | 49   | 0.7847          | 97.0677     | 97.9841  |
| 1.1811        | 0.2673 | 50   | 0.7324          | 97.5940     | 98.4006  |
| 0.9734        | 0.2726 | 51   | 0.6814          | 97.7444     | 98.5321  |
| 0.928         | 0.2780 | 52   | 0.6318          | 97.8947     | 98.6140  |
| 0.8989        | 0.2833 | 53   | 0.5859          | 98.1203     | 98.7571  |
| 0.7784        | 0.2887 | 54   | 0.5430          | 98.3459     | 98.9243  |
| 1.0015        | 0.2940 | 55   | 0.5027          | 98.3459     | 98.8914  |
| 0.7509        | 0.2994 | 56   | 0.4656          | 98.5714     | 99.0811  |
| 0.6838        | 0.3047 | 57   | 0.4328          | 98.7970     | 99.1723  |
| 0.7336        | 0.3101 | 58   | 0.4042          | 98.8722     | 99.1327  |
| 0.5729        | 0.3154 | 59   | 0.3781          | 98.9474     | 99.2079  |
| 0.5891        | 0.3207 | 60   | 0.3538          | 99.0226     | 99.3362  |
| 0.6168        | 0.3261 | 61   | 0.3322          | 99.1729     | 99.4169  |
| 0.5503        | 0.3314 | 62   | 0.3130          | 99.1729     | 99.4169  |
| 0.5058        | 0.3368 | 63   | 0.2955          | 99.1729     | 99.4169  |
| 0.4065        | 0.3421 | 64   | 0.2788          | 99.3233     | 99.5000  |
| 0.4466        | 0.3475 | 65   | 0.2638          | 99.2481     | 99.4981  |
| 0.4727        | 0.3528 | 66   | 0.2496          | 99.2481     | 99.4981  |
| 0.45          | 0.3582 | 67   | 0.2365          | 99.2481     | 99.4981  |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3