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---
library_name: peft
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: tamil-qa-distilled
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. -->
# tamil-qa-distilled
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3458
- Exact: 23.9056
- F1: 40.0870
- Total: 5848
- Hasans Exact: 23.9056
- Hasans F1: 40.0870
- Hasans Total: 5848
- Best Exact: 23.9056
- Best Exact Thresh: 0.0
- Best F1: 40.0870
- Best F1 Thresh: 0.0
## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Best Exact | Best Exact Thresh | Best F1 | Best F1 Thresh |
|:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:----------:|:-----------------:|:-------:|:--------------:|
| 5.9372 | 0.0302 | 250 | 5.5830 | 0.0342 | 5.2752 | 5848 | 0.0342 | 5.2752 | 5848 | 0.0342 | 0.0 | 5.2752 | 0.0 |
| 4.4435 | 0.0604 | 500 | 4.1190 | 5.5404 | 14.4289 | 5848 | 5.5404 | 14.4289 | 5848 | 5.5404 | 0.0 | 14.4289 | 0.0 |
| 4.1362 | 0.0905 | 750 | 3.8871 | 11.4227 | 22.5581 | 5848 | 11.4227 | 22.5581 | 5848 | 11.4227 | 0.0 | 22.5581 | 0.0 |
| 4.071 | 0.1207 | 1000 | 3.6548 | 12.6197 | 23.8673 | 5848 | 12.6197 | 23.8673 | 5848 | 12.6197 | 0.0 | 23.8673 | 0.0 |
| 3.4979 | 0.1509 | 1250 | 3.3795 | 12.7052 | 23.1488 | 5848 | 12.7052 | 23.1488 | 5848 | 12.7052 | 0.0 | 23.1488 | 0.0 |
| 3.5602 | 0.1811 | 1500 | 3.1890 | 16.9631 | 28.6981 | 5848 | 16.9631 | 28.6981 | 5848 | 16.9631 | 0.0 | 28.6981 | 0.0 |
| 3.1678 | 0.2112 | 1750 | 3.0806 | 17.1512 | 31.1741 | 5848 | 17.1512 | 31.1741 | 5848 | 17.1512 | 0.0 | 31.1741 | 0.0 |
| 3.3402 | 0.2414 | 2000 | 3.0524 | 20.2975 | 34.5701 | 5848 | 20.2975 | 34.5701 | 5848 | 20.2975 | 0.0 | 34.5701 | 0.0 |
| 3.2099 | 0.2716 | 2250 | 2.8973 | 17.4761 | 30.0263 | 5848 | 17.4761 | 30.0263 | 5848 | 17.4761 | 0.0 | 30.0263 | 0.0 |
| 2.9404 | 0.3018 | 2500 | 2.9263 | 20.3830 | 35.2615 | 5848 | 20.3830 | 35.2615 | 5848 | 20.3830 | 0.0 | 35.2615 | 0.0 |
| 3.5376 | 0.3319 | 2750 | 2.8066 | 19.1518 | 33.0098 | 5848 | 19.1518 | 33.0098 | 5848 | 19.1518 | 0.0 | 33.0098 | 0.0 |
| 2.8949 | 0.3621 | 3000 | 2.7580 | 18.1601 | 30.1449 | 5848 | 18.1601 | 30.1449 | 5848 | 18.1601 | 0.0 | 30.1449 | 0.0 |
| 3.1584 | 0.3923 | 3250 | 2.8535 | 21.9904 | 37.2070 | 5848 | 21.9904 | 37.2070 | 5848 | 21.9904 | 0.0 | 37.2070 | 0.0 |
| 2.7833 | 0.4225 | 3500 | 2.8549 | 21.7339 | 36.0716 | 5848 | 21.7339 | 36.0716 | 5848 | 21.7339 | 0.0 | 36.0716 | 0.0 |
| 2.9223 | 0.4526 | 3750 | 2.7775 | 19.8529 | 33.9325 | 5848 | 19.8529 | 33.9325 | 5848 | 19.8529 | 0.0 | 33.9325 | 0.0 |
| 3.0183 | 0.4828 | 4000 | 2.6326 | 17.8865 | 29.0245 | 5848 | 17.8865 | 29.0245 | 5848 | 17.8865 | 0.0 | 29.0245 | 0.0 |
| 2.8363 | 0.5130 | 4250 | 2.7080 | 22.2298 | 37.7239 | 5848 | 22.2298 | 37.7239 | 5848 | 22.2298 | 0.0 | 37.7239 | 0.0 |
| 2.7222 | 0.5432 | 4500 | 2.8187 | 22.1785 | 38.5146 | 5848 | 22.1785 | 38.5146 | 5848 | 22.1785 | 0.0 | 38.5146 | 0.0 |
| 2.6763 | 0.5733 | 4750 | 2.6953 | 22.5547 | 37.7873 | 5848 | 22.5547 | 37.7873 | 5848 | 22.5547 | 0.0 | 37.7873 | 0.0 |
| 2.5925 | 0.6035 | 5000 | 2.8178 | 22.6573 | 38.2837 | 5848 | 22.6573 | 38.2837 | 5848 | 22.6573 | 0.0 | 38.2837 | 0.0 |
| 2.6485 | 0.6337 | 5250 | 2.6554 | 22.1101 | 37.5204 | 5848 | 22.1101 | 37.5204 | 5848 | 22.1101 | 0.0 | 37.5204 | 0.0 |
| 3.0327 | 0.6639 | 5500 | 2.6043 | 21.9733 | 37.8703 | 5848 | 21.9733 | 37.8703 | 5848 | 21.9733 | 0.0 | 37.8703 | 0.0 |
| 2.7016 | 0.6940 | 5750 | 2.5752 | 22.1272 | 38.2156 | 5848 | 22.1272 | 38.2156 | 5848 | 22.1272 | 0.0 | 38.2156 | 0.0 |
| 2.6785 | 0.7242 | 6000 | 2.6829 | 22.5889 | 38.4805 | 5848 | 22.5889 | 38.4805 | 5848 | 22.5889 | 0.0 | 38.4805 | 0.0 |
| 2.7414 | 0.7544 | 6250 | 2.5243 | 21.8023 | 36.6811 | 5848 | 21.8023 | 36.6811 | 5848 | 21.8023 | 0.0 | 36.6811 | 0.0 |
| 2.6025 | 0.7846 | 6500 | 2.4759 | 22.2811 | 36.9185 | 5848 | 22.2811 | 36.9185 | 5848 | 22.2811 | 0.0 | 36.9185 | 0.0 |
| 2.6305 | 0.8147 | 6750 | 2.6849 | 23.3926 | 39.6983 | 5848 | 23.3926 | 39.6983 | 5848 | 23.3926 | 0.0 | 39.6983 | 0.0 |
| 2.6485 | 0.8449 | 7000 | 2.5377 | 23.2900 | 38.9523 | 5848 | 23.2900 | 38.9523 | 5848 | 23.2900 | 0.0 | 38.9523 | 0.0 |
| 2.6644 | 0.8751 | 7250 | 2.5613 | 22.6915 | 37.9573 | 5848 | 22.6915 | 37.9573 | 5848 | 22.6915 | 0.0 | 37.9573 | 0.0 |
| 2.4504 | 0.9053 | 7500 | 2.5179 | 23.1019 | 39.2826 | 5848 | 23.1019 | 39.2826 | 5848 | 23.1019 | 0.0 | 39.2826 | 0.0 |
| 2.7274 | 0.9354 | 7750 | 2.4839 | 23.3242 | 39.6564 | 5848 | 23.3242 | 39.6564 | 5848 | 23.3242 | 0.0 | 39.6564 | 0.0 |
| 2.732 | 0.9656 | 8000 | 2.4473 | 22.6915 | 38.1896 | 5848 | 22.6915 | 38.1896 | 5848 | 22.6915 | 0.0 | 38.1896 | 0.0 |
| 2.5412 | 0.9958 | 8250 | 2.4536 | 22.8112 | 38.1155 | 5848 | 22.8112 | 38.1155 | 5848 | 22.8112 | 0.0 | 38.1155 | 0.0 |
| 2.4763 | 1.0260 | 8500 | 2.4868 | 23.6491 | 39.5944 | 5848 | 23.6491 | 39.5944 | 5848 | 23.6491 | 0.0 | 39.5944 | 0.0 |
| 2.6547 | 1.0561 | 8750 | 2.5451 | 23.8030 | 40.6998 | 5848 | 23.8030 | 40.6998 | 5848 | 23.8030 | 0.0 | 40.6998 | 0.0 |
| 2.5899 | 1.0863 | 9000 | 2.4332 | 22.6915 | 37.8246 | 5848 | 22.6915 | 37.8246 | 5848 | 22.6915 | 0.0 | 37.8246 | 0.0 |
| 2.5589 | 1.1165 | 9250 | 2.3779 | 22.0417 | 36.8825 | 5848 | 22.0417 | 36.8825 | 5848 | 22.0417 | 0.0 | 36.8825 | 0.0 |
| 2.3319 | 1.1467 | 9500 | 2.3715 | 22.7086 | 38.7405 | 5848 | 22.7086 | 38.7405 | 5848 | 22.7086 | 0.0 | 38.7405 | 0.0 |
| 2.2501 | 1.1768 | 9750 | 2.4244 | 23.4781 | 39.4466 | 5848 | 23.4781 | 39.4466 | 5848 | 23.4781 | 0.0 | 39.4466 | 0.0 |
| 2.337 | 1.2070 | 10000 | 2.3661 | 22.5718 | 36.8759 | 5848 | 22.5718 | 36.8759 | 5848 | 22.5718 | 0.0 | 36.8759 | 0.0 |
| 2.5074 | 1.2372 | 10250 | 2.6080 | 25.1368 | 42.1434 | 5848 | 25.1368 | 42.1434 | 5848 | 25.1368 | 0.0 | 42.1434 | 0.0 |
| 2.531 | 1.2674 | 10500 | 2.3560 | 21.6484 | 36.3479 | 5848 | 21.6484 | 36.3479 | 5848 | 21.6484 | 0.0 | 36.3479 | 0.0 |
| 2.513 | 1.2975 | 10750 | 2.4430 | 23.2387 | 39.3702 | 5848 | 23.2387 | 39.3702 | 5848 | 23.2387 | 0.0 | 39.3702 | 0.0 |
| 2.2461 | 1.3277 | 11000 | 2.5602 | 23.6149 | 40.3775 | 5848 | 23.6149 | 40.3775 | 5848 | 23.6149 | 0.0 | 40.3775 | 0.0 |
| 2.5934 | 1.3579 | 11250 | 2.4806 | 22.8625 | 39.5232 | 5848 | 22.8625 | 39.5232 | 5848 | 22.8625 | 0.0 | 39.5232 | 0.0 |
| 2.2671 | 1.3881 | 11500 | 2.4027 | 22.7599 | 37.6504 | 5848 | 22.7599 | 37.6504 | 5848 | 22.7599 | 0.0 | 37.6504 | 0.0 |
| 2.3451 | 1.4182 | 11750 | 2.4227 | 23.1703 | 39.2540 | 5848 | 23.1703 | 39.2540 | 5848 | 23.1703 | 0.0 | 39.2540 | 0.0 |
| 2.6997 | 1.4484 | 12000 | 2.3992 | 23.2216 | 39.6402 | 5848 | 23.2216 | 39.6402 | 5848 | 23.2216 | 0.0 | 39.6402 | 0.0 |
| 2.5253 | 1.4786 | 12250 | 2.4076 | 24.1621 | 40.3112 | 5848 | 24.1621 | 40.3112 | 5848 | 24.1621 | 0.0 | 40.3112 | 0.0 |
| 2.6091 | 1.5088 | 12500 | 2.3743 | 22.4179 | 38.6880 | 5848 | 22.4179 | 38.6880 | 5848 | 22.4179 | 0.0 | 38.6880 | 0.0 |
| 2.3445 | 1.5389 | 12750 | 2.3862 | 23.9056 | 39.8296 | 5848 | 23.9056 | 39.8296 | 5848 | 23.9056 | 0.0 | 39.8296 | 0.0 |
| 2.4083 | 1.5691 | 13000 | 2.5380 | 24.8461 | 41.8272 | 5848 | 24.8461 | 41.8272 | 5848 | 24.8461 | 0.0 | 41.8272 | 0.0 |
| 2.2722 | 1.5993 | 13250 | 2.3600 | 23.5123 | 39.5265 | 5848 | 23.5123 | 39.5265 | 5848 | 23.5123 | 0.0 | 39.5265 | 0.0 |
| 2.4945 | 1.6295 | 13500 | 2.3862 | 23.9056 | 39.8474 | 5848 | 23.9056 | 39.8474 | 5848 | 23.9056 | 0.0 | 39.8474 | 0.0 |
| 2.5895 | 1.6596 | 13750 | 2.3518 | 23.4952 | 39.1746 | 5848 | 23.4952 | 39.1746 | 5848 | 23.4952 | 0.0 | 39.1746 | 0.0 |
| 2.3354 | 1.6898 | 14000 | 2.3872 | 23.9398 | 39.7158 | 5848 | 23.9398 | 39.7158 | 5848 | 23.9398 | 0.0 | 39.7158 | 0.0 |
| 2.4015 | 1.7200 | 14250 | 2.3248 | 23.4439 | 39.5145 | 5848 | 23.4439 | 39.5145 | 5848 | 23.4439 | 0.0 | 39.5145 | 0.0 |
| 2.4879 | 1.7502 | 14500 | 2.3493 | 24.1279 | 40.3504 | 5848 | 24.1279 | 40.3504 | 5848 | 24.1279 | 0.0 | 40.3504 | 0.0 |
| 2.1585 | 1.7803 | 14750 | 2.3772 | 24.0424 | 40.2189 | 5848 | 24.0424 | 40.2189 | 5848 | 24.0424 | 0.0 | 40.2189 | 0.0 |
| 2.307 | 1.8105 | 15000 | 2.3749 | 23.8543 | 40.0715 | 5848 | 23.8543 | 40.0715 | 5848 | 23.8543 | 0.0 | 40.0715 | 0.0 |
| 2.2712 | 1.8407 | 15250 | 2.3785 | 24.1279 | 40.3913 | 5848 | 24.1279 | 40.3913 | 5848 | 24.1279 | 0.0 | 40.3913 | 0.0 |
| 2.2997 | 1.8709 | 15500 | 2.3398 | 23.6320 | 39.4552 | 5848 | 23.6320 | 39.4552 | 5848 | 23.6320 | 0.0 | 39.4552 | 0.0 |
| 2.7463 | 1.9010 | 15750 | 2.3623 | 23.9227 | 39.9884 | 5848 | 23.9227 | 39.9884 | 5848 | 23.9227 | 0.0 | 39.9884 | 0.0 |
| 2.6714 | 1.9312 | 16000 | 2.3396 | 24.0424 | 40.0280 | 5848 | 24.0424 | 40.0280 | 5848 | 24.0424 | 0.0 | 40.0280 | 0.0 |
| 2.4979 | 1.9614 | 16250 | 2.3225 | 23.7004 | 39.5895 | 5848 | 23.7004 | 39.5895 | 5848 | 23.7004 | 0.0 | 39.5895 | 0.0 |
| 2.4623 | 1.9916 | 16500 | 2.3458 | 23.9056 | 40.0870 | 5848 | 23.9056 | 40.0870 | 5848 | 23.9056 | 0.0 | 40.0870 | 0.0 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.48.2
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0 |