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---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fine_tuned_XLMROBERTA_cs_wikann
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. -->
# fine_tuned_XLMROBERTA_cs_wikann
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1699
- Precision: 0.9133
- Recall: 0.9319
- F1: 0.9225
- Accuracy: 0.9699
## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7699 | 0.2 | 500 | 0.3588 | 0.5878 | 0.6990 | 0.6386 | 0.8894 |
| 0.3658 | 0.4 | 1000 | 0.2538 | 0.7427 | 0.8258 | 0.7821 | 0.9355 |
| 0.301 | 0.6 | 1500 | 0.2403 | 0.7649 | 0.8237 | 0.7932 | 0.9400 |
| 0.2796 | 0.8 | 2000 | 0.1828 | 0.7967 | 0.8509 | 0.8229 | 0.9456 |
| 0.258 | 1.0 | 2500 | 0.2223 | 0.7770 | 0.8322 | 0.8037 | 0.9400 |
| 0.2192 | 1.2 | 3000 | 0.1911 | 0.8156 | 0.8745 | 0.8440 | 0.9511 |
| 0.2161 | 1.4 | 3500 | 0.1878 | 0.8401 | 0.8858 | 0.8623 | 0.9551 |
| 0.2095 | 1.6 | 4000 | 0.1916 | 0.8306 | 0.8783 | 0.8538 | 0.9559 |
| 0.2137 | 1.8 | 4500 | 0.1657 | 0.8573 | 0.8874 | 0.8721 | 0.9585 |
| 0.1884 | 2.0 | 5000 | 0.2134 | 0.8486 | 0.8837 | 0.8658 | 0.9542 |
| 0.164 | 2.2 | 5500 | 0.2038 | 0.8619 | 0.9048 | 0.8828 | 0.9588 |
| 0.1564 | 2.4 | 6000 | 0.1707 | 0.8502 | 0.8874 | 0.8684 | 0.9582 |
| 0.1719 | 2.6 | 6500 | 0.1781 | 0.8645 | 0.8994 | 0.8816 | 0.9610 |
| 0.1565 | 2.8 | 7000 | 0.1908 | 0.8712 | 0.9021 | 0.8864 | 0.9614 |
| 0.1713 | 3.0 | 7500 | 0.1628 | 0.8672 | 0.8954 | 0.8811 | 0.9623 |
| 0.1359 | 3.2 | 8000 | 0.1890 | 0.8684 | 0.9072 | 0.8874 | 0.9624 |
| 0.1362 | 3.4 | 8500 | 0.1672 | 0.8653 | 0.9065 | 0.8854 | 0.9620 |
| 0.1301 | 3.6 | 9000 | 0.1866 | 0.8698 | 0.9069 | 0.8879 | 0.9631 |
| 0.1345 | 3.8 | 9500 | 0.1766 | 0.8759 | 0.9071 | 0.8913 | 0.9647 |
| 0.1363 | 4.0 | 10000 | 0.1817 | 0.8700 | 0.9137 | 0.8913 | 0.9626 |
| 0.1097 | 4.2 | 10500 | 0.1611 | 0.8861 | 0.9118 | 0.8987 | 0.9653 |
| 0.1045 | 4.4 | 11000 | 0.1743 | 0.8899 | 0.9123 | 0.9009 | 0.9659 |
| 0.1068 | 4.6 | 11500 | 0.1771 | 0.8870 | 0.9167 | 0.9016 | 0.9660 |
| 0.1168 | 4.8 | 12000 | 0.1704 | 0.8894 | 0.9174 | 0.9032 | 0.9660 |
| 0.1116 | 5.0 | 12500 | 0.1748 | 0.8926 | 0.9203 | 0.9062 | 0.9673 |
| 0.0979 | 5.2 | 13000 | 0.1726 | 0.8956 | 0.9255 | 0.9103 | 0.9672 |
| 0.0992 | 5.4 | 13500 | 0.1798 | 0.9058 | 0.9280 | 0.9168 | 0.9686 |
| 0.0929 | 5.6 | 14000 | 0.1740 | 0.9063 | 0.9304 | 0.9182 | 0.9693 |
| 0.098 | 5.8 | 14500 | 0.1690 | 0.8931 | 0.9262 | 0.9094 | 0.9683 |
| 0.0878 | 6.0 | 15000 | 0.1682 | 0.9065 | 0.9294 | 0.9178 | 0.9696 |
| 0.0925 | 6.2 | 15500 | 0.1691 | 0.9102 | 0.9308 | 0.9204 | 0.9694 |
| 0.0841 | 6.4 | 16000 | 0.1657 | 0.9138 | 0.9298 | 0.9217 | 0.9699 |
| 0.0748 | 6.6 | 16500 | 0.1696 | 0.9114 | 0.9313 | 0.9213 | 0.9695 |
| 0.0753 | 6.8 | 17000 | 0.1703 | 0.9118 | 0.9311 | 0.9214 | 0.9697 |
| 0.073 | 7.0 | 17500 | 0.1699 | 0.9133 | 0.9319 | 0.9225 | 0.9699 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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