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---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: TUF_XLNET_5E
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. -->
# TUF_XLNET_5E
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2725
- Accuracy: 0.9533
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4817 | 0.1 | 50 | 0.2602 | 0.8733 |
| 0.2405 | 0.2 | 100 | 0.5818 | 0.88 |
| 0.2172 | 0.3 | 150 | 0.1851 | 0.9533 |
| 0.2697 | 0.4 | 200 | 0.1692 | 0.9267 |
| 0.2313 | 0.5 | 250 | 0.1086 | 0.9467 |
| 0.2245 | 0.59 | 300 | 0.2031 | 0.9267 |
| 0.1805 | 0.69 | 350 | 0.1414 | 0.9467 |
| 0.1896 | 0.79 | 400 | 0.0824 | 0.9733 |
| 0.1969 | 0.89 | 450 | 0.1499 | 0.9533 |
| 0.1745 | 0.99 | 500 | 0.1827 | 0.9267 |
| 0.1143 | 1.09 | 550 | 0.1923 | 0.9533 |
| 0.1478 | 1.19 | 600 | 0.1718 | 0.94 |
| 0.1368 | 1.29 | 650 | 0.1170 | 0.9733 |
| 0.1288 | 1.39 | 700 | 0.1418 | 0.9667 |
| 0.1689 | 1.49 | 750 | 0.1173 | 0.9733 |
| 0.1078 | 1.58 | 800 | 0.2784 | 0.9333 |
| 0.1343 | 1.68 | 850 | 0.1555 | 0.9533 |
| 0.1104 | 1.78 | 900 | 0.1361 | 0.9533 |
| 0.1267 | 1.88 | 950 | 0.1936 | 0.9267 |
| 0.0928 | 1.98 | 1000 | 0.3070 | 0.94 |
| 0.0949 | 2.08 | 1050 | 0.1905 | 0.94 |
| 0.0329 | 2.18 | 1100 | 0.2296 | 0.9533 |
| 0.0406 | 2.28 | 1150 | 0.3202 | 0.94 |
| 0.0983 | 2.38 | 1200 | 0.4515 | 0.9267 |
| 0.0533 | 2.48 | 1250 | 0.2152 | 0.9533 |
| 0.0878 | 2.57 | 1300 | 0.1573 | 0.9533 |
| 0.0595 | 2.67 | 1350 | 0.1699 | 0.96 |
| 0.0937 | 2.77 | 1400 | 0.2825 | 0.9333 |
| 0.0817 | 2.87 | 1450 | 0.2325 | 0.9467 |
| 0.0845 | 2.97 | 1500 | 0.1918 | 0.9533 |
| 0.0711 | 3.07 | 1550 | 0.3186 | 0.94 |
| 0.033 | 3.17 | 1600 | 0.2571 | 0.94 |
| 0.0134 | 3.27 | 1650 | 0.2733 | 0.94 |
| 0.0546 | 3.37 | 1700 | 0.1934 | 0.9533 |
| 0.0277 | 3.47 | 1750 | 0.2731 | 0.94 |
| 0.0081 | 3.56 | 1800 | 0.2531 | 0.9467 |
| 0.0387 | 3.66 | 1850 | 0.2121 | 0.96 |
| 0.0014 | 3.76 | 1900 | 0.2601 | 0.96 |
| 0.0379 | 3.86 | 1950 | 0.2501 | 0.9467 |
| 0.0271 | 3.96 | 2000 | 0.2899 | 0.94 |
| 0.0182 | 4.06 | 2050 | 0.2197 | 0.9533 |
| 0.0263 | 4.16 | 2100 | 0.2374 | 0.9533 |
| 0.0079 | 4.26 | 2150 | 0.3192 | 0.94 |
| 0.0239 | 4.36 | 2200 | 0.3755 | 0.9333 |
| 0.02 | 4.46 | 2250 | 0.2702 | 0.9467 |
| 0.0072 | 4.55 | 2300 | 0.2055 | 0.9533 |
| 0.0124 | 4.65 | 2350 | 0.2299 | 0.9533 |
| 0.0072 | 4.75 | 2400 | 0.2813 | 0.9533 |
| 0.0125 | 4.85 | 2450 | 0.2696 | 0.9533 |
| 0.0205 | 4.95 | 2500 | 0.2725 | 0.9533 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2
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