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---
library_name: transformers
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
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. -->
# test
This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2434
- Precision: 0.9144
- Recall: 0.9105
- F1: 0.9124
- Accuracy: 0.9725
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.7937 | 100 | 0.1878 | 0.8406 | 0.8761 | 0.8580 | 0.9542 |
| No log | 1.5873 | 200 | 0.1337 | 0.8943 | 0.8864 | 0.8903 | 0.9650 |
| No log | 2.3810 | 300 | 0.1259 | 0.9020 | 0.9214 | 0.9116 | 0.9716 |
| No log | 3.1746 | 400 | 0.1317 | 0.9100 | 0.9181 | 0.9140 | 0.9730 |
| 0.2107 | 3.9683 | 500 | 0.1159 | 0.9144 | 0.9065 | 0.9104 | 0.9710 |
| 0.2107 | 4.7619 | 600 | 0.1169 | 0.9147 | 0.9072 | 0.9109 | 0.9715 |
| 0.2107 | 5.5556 | 700 | 0.1240 | 0.9025 | 0.9144 | 0.9084 | 0.9712 |
| 0.2107 | 6.3492 | 800 | 0.1351 | 0.9160 | 0.9118 | 0.9139 | 0.9727 |
| 0.2107 | 7.1429 | 900 | 0.1469 | 0.9207 | 0.9055 | 0.9131 | 0.9722 |
| 0.0518 | 7.9365 | 1000 | 0.1333 | 0.9053 | 0.9158 | 0.9105 | 0.9717 |
| 0.0518 | 8.7302 | 1100 | 0.1367 | 0.9119 | 0.9167 | 0.9143 | 0.9724 |
| 0.0518 | 9.5238 | 1200 | 0.1412 | 0.9057 | 0.9134 | 0.9095 | 0.9712 |
| 0.0518 | 10.3175 | 1300 | 0.1666 | 0.9203 | 0.9158 | 0.9180 | 0.9740 |
| 0.0518 | 11.1111 | 1400 | 0.1610 | 0.9050 | 0.9062 | 0.9056 | 0.9707 |
| 0.0316 | 11.9048 | 1500 | 0.1677 | 0.9175 | 0.9111 | 0.9143 | 0.9720 |
| 0.0316 | 12.6984 | 1600 | 0.1838 | 0.9097 | 0.9052 | 0.9074 | 0.9715 |
| 0.0316 | 13.4921 | 1700 | 0.1622 | 0.9182 | 0.9082 | 0.9131 | 0.9725 |
| 0.0316 | 14.2857 | 1800 | 0.1855 | 0.9161 | 0.9092 | 0.9126 | 0.9725 |
| 0.0316 | 15.0794 | 1900 | 0.1739 | 0.9078 | 0.9171 | 0.9124 | 0.9725 |
| 0.0174 | 15.8730 | 2000 | 0.1902 | 0.9167 | 0.9167 | 0.9167 | 0.9734 |
| 0.0174 | 16.6667 | 2100 | 0.1729 | 0.9207 | 0.9171 | 0.9189 | 0.9739 |
| 0.0174 | 17.4603 | 2200 | 0.2083 | 0.9147 | 0.9171 | 0.9159 | 0.9734 |
| 0.0174 | 18.2540 | 2300 | 0.2233 | 0.9108 | 0.9177 | 0.9143 | 0.9724 |
| 0.0174 | 19.0476 | 2400 | 0.2165 | 0.9201 | 0.9134 | 0.9168 | 0.9730 |
| 0.0085 | 19.8413 | 2500 | 0.2138 | 0.9117 | 0.9111 | 0.9114 | 0.9721 |
| 0.0085 | 20.6349 | 2600 | 0.2109 | 0.9150 | 0.9108 | 0.9129 | 0.9725 |
| 0.0085 | 21.4286 | 2700 | 0.2118 | 0.9216 | 0.9167 | 0.9192 | 0.9742 |
| 0.0085 | 22.2222 | 2800 | 0.2287 | 0.9184 | 0.9184 | 0.9184 | 0.9742 |
| 0.0085 | 23.0159 | 2900 | 0.2350 | 0.9118 | 0.9085 | 0.9101 | 0.9719 |
| 0.0043 | 23.8095 | 3000 | 0.2406 | 0.9109 | 0.9158 | 0.9133 | 0.9727 |
| 0.0043 | 24.6032 | 3100 | 0.2480 | 0.9105 | 0.9072 | 0.9088 | 0.9715 |
| 0.0043 | 25.3968 | 3200 | 0.2430 | 0.9112 | 0.9055 | 0.9084 | 0.9714 |
| 0.0043 | 26.1905 | 3300 | 0.2396 | 0.9092 | 0.9068 | 0.9080 | 0.9712 |
| 0.0043 | 26.9841 | 3400 | 0.2386 | 0.9152 | 0.9164 | 0.9158 | 0.9732 |
| 0.0026 | 27.7778 | 3500 | 0.2417 | 0.9123 | 0.9111 | 0.9117 | 0.9720 |
| 0.0026 | 28.5714 | 3600 | 0.2433 | 0.9136 | 0.9085 | 0.9110 | 0.9721 |
| 0.0026 | 29.3651 | 3700 | 0.2434 | 0.9144 | 0.9105 | 0.9124 | 0.9725 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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