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---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta_AGRO
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. -->
# xlm-roberta_AGRO
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1553
- Exact Match: 28.8571
- F1 Score: 57.3777
## 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: 1e-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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 Score |
|:-------------:|:------:|:----:|:---------------:|:-----------:|:--------:|
| 5.8392 | 0.0053 | 1 | 5.8105 | 0.0 | 8.3419 |
| 5.8343 | 0.0107 | 2 | 5.7849 | 0.0 | 8.4316 |
| 5.7993 | 0.0160 | 3 | 5.7441 | 0.0 | 8.4504 |
| 5.7869 | 0.0214 | 4 | 5.6912 | 0.0 | 8.5621 |
| 5.7257 | 0.0267 | 5 | 5.5845 | 0.0 | 8.7289 |
| 5.6119 | 0.0321 | 6 | 5.3128 | 0.0 | 9.0780 |
| 5.4565 | 0.0374 | 7 | 5.0020 | 0.0 | 9.9612 |
| 5.3734 | 0.0428 | 8 | 4.8134 | 0.2256 | 11.2348 |
| 5.276 | 0.0481 | 9 | 4.6671 | 0.3008 | 14.1031 |
| 5.1939 | 0.0535 | 10 | 4.5397 | 0.6767 | 18.7252 |
| 5.1236 | 0.0588 | 11 | 4.4828 | 1.5789 | 24.3832 |
| 4.9665 | 0.0641 | 12 | 4.4752 | 2.8571 | 30.7203 |
| 4.9217 | 0.0695 | 13 | 4.4690 | 3.8346 | 35.5977 |
| 4.7769 | 0.0748 | 14 | 4.4478 | 4.6617 | 40.6799 |
| 4.7217 | 0.0802 | 15 | 4.4240 | 6.5414 | 45.6464 |
| 4.6351 | 0.0855 | 16 | 4.4011 | 7.7444 | 48.5045 |
| 4.6804 | 0.0909 | 17 | 4.3807 | 8.5714 | 50.6061 |
| 4.5868 | 0.0962 | 18 | 4.3575 | 9.9248 | 51.9678 |
| 4.6173 | 0.1016 | 19 | 4.3315 | 11.4286 | 53.0364 |
| 4.5892 | 0.1069 | 20 | 4.3008 | 12.9323 | 54.0593 |
| 4.5701 | 0.1123 | 21 | 4.2633 | 14.5113 | 54.4900 |
| 4.6351 | 0.1176 | 22 | 4.2220 | 16.8421 | 55.2744 |
| 4.429 | 0.1230 | 23 | 4.1739 | 17.7444 | 55.4881 |
| 4.4307 | 0.1283 | 24 | 4.1211 | 18.7970 | 55.8574 |
| 4.329 | 0.1336 | 25 | 4.0618 | 19.4737 | 55.9693 |
| 4.3469 | 0.1390 | 26 | 3.9998 | 20.8271 | 56.2430 |
| 4.2975 | 0.1443 | 27 | 3.9353 | 21.9549 | 56.6353 |
| 4.3071 | 0.1497 | 28 | 3.8643 | 23.6842 | 57.2411 |
| 4.1047 | 0.1550 | 29 | 3.7867 | 24.2857 | 57.1472 |
| 4.1373 | 0.1604 | 30 | 3.7061 | 25.2632 | 57.1898 |
| 4.1239 | 0.1657 | 31 | 3.6258 | 26.4662 | 57.5706 |
| 3.959 | 0.1711 | 32 | 3.5479 | 27.5188 | 57.7931 |
| 3.7877 | 0.1764 | 33 | 3.4758 | 28.0451 | 57.7765 |
| 3.8374 | 0.1818 | 34 | 3.4128 | 29.0226 | 58.1740 |
| 3.8019 | 0.1871 | 35 | 3.3584 | 30.1504 | 58.6550 |
| 3.7692 | 0.1924 | 36 | 3.3101 | 30.8271 | 58.9151 |
| 3.6371 | 0.1978 | 37 | 3.2669 | 32.0301 | 59.2735 |
| 3.8148 | 0.2031 | 38 | 3.2291 | 32.1805 | 59.2572 |
| 3.7487 | 0.2085 | 39 | 3.1963 | 32.5564 | 59.2920 |
| 3.6244 | 0.2138 | 40 | 3.1672 | 32.1053 | 59.4804 |
| 3.6659 | 0.2192 | 41 | 3.1432 | 31.2030 | 59.2762 |
| 3.4609 | 0.2245 | 42 | 3.1235 | 30.0752 | 59.1610 |
| 3.5882 | 0.2299 | 43 | 3.1077 | 29.5489 | 58.9967 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3
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