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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: aus_slang_classifier
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. -->
# aus_slang_classifier
This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 0.487
## 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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.0005 | 1.0 | 1250 | 0.0002 | 0.487 |
| 0.001 | 2.0 | 2500 | 0.0002 | 0.487 |
| 0.0088 | 3.0 | 3750 | 0.0012 | 0.487 |
| 0.0035 | 4.0 | 5000 | 0.0027 | 0.487 |
| 0.0061 | 5.0 | 6250 | 0.0016 | 0.487 |
| 0.0003 | 6.0 | 7500 | 0.0000 | 0.487 |
| 0.0003 | 7.0 | 8750 | 0.0001 | 0.487 |
| 0.0003 | 8.0 | 10000 | 0.0000 | 0.487 |
| 0.0003 | 9.0 | 11250 | 0.0000 | 0.487 |
| 0.0016 | 10.0 | 12500 | 0.0004 | 0.487 |
| 0.0005 | 11.0 | 13750 | 0.0000 | 0.487 |
| 0.0011 | 12.0 | 15000 | 0.0000 | 0.487 |
| 0.0002 | 13.0 | 16250 | 0.0000 | 0.487 |
| 0.0002 | 14.0 | 17500 | 0.0001 | 0.487 |
| 0.0002 | 15.0 | 18750 | 0.0000 | 0.487 |
| 0.0002 | 16.0 | 20000 | 0.0002 | 0.487 |
| 0.0002 | 17.0 | 21250 | 0.0000 | 0.487 |
| 0.0002 | 18.0 | 22500 | 0.0004 | 0.487 |
| 0.0005 | 19.0 | 23750 | 0.0000 | 0.487 |
| 0.0002 | 20.0 | 25000 | 0.0001 | 0.487 |
| 0.0002 | 21.0 | 26250 | 0.0000 | 0.487 |
| 0.0001 | 22.0 | 27500 | 0.0000 | 0.487 |
| 0.0015 | 23.0 | 28750 | 0.0004 | 0.487 |
| 0.0011 | 24.0 | 30000 | 0.0001 | 0.487 |
| 0.0007 | 25.0 | 31250 | 0.0061 | 0.487 |
| 0.0012 | 26.0 | 32500 | 0.0025 | 0.487 |
| 0.0015 | 27.0 | 33750 | 0.0060 | 0.487 |
| 0.0018 | 28.0 | 35000 | 0.0051 | 0.487 |
| 0.0022 | 29.0 | 36250 | 0.0050 | 0.487 |
| 0.0024 | 30.0 | 37500 | 0.0051 | 0.487 |
| 0.0025 | 31.0 | 38750 | 0.0020 | 0.487 |
| 0.0007 | 32.0 | 40000 | 0.0021 | 0.487 |
| 0.0013 | 33.0 | 41250 | 0.0021 | 0.487 |
| 0.0018 | 34.0 | 42500 | 0.0020 | 0.487 |
| 0.0013 | 35.0 | 43750 | 0.0027 | 0.487 |
| 0.0013 | 36.0 | 45000 | 0.0020 | 0.487 |
| 0.001 | 37.0 | 46250 | 0.0020 | 0.487 |
| 0.0007 | 38.0 | 47500 | 0.0022 | 0.487 |
| 0.0017 | 39.0 | 48750 | 0.0022 | 0.487 |
| 0.0017 | 40.0 | 50000 | 0.0021 | 0.487 |
| 0.0048 | 41.0 | 51250 | 0.0041 | 0.487 |
| 0.0012 | 42.0 | 52500 | 0.0020 | 0.487 |
| 0.0015 | 43.0 | 53750 | 0.0020 | 0.487 |
| 0.0017 | 44.0 | 55000 | 0.0023 | 0.487 |
| 0.0038 | 45.0 | 56250 | 0.0021 | 0.487 |
| 0.0032 | 46.0 | 57500 | 0.0021 | 0.487 |
| 0.0343 | 47.0 | 58750 | 0.2751 | 0.487 |
| 0.0012 | 48.0 | 60000 | 0.0013 | 0.487 |
| 0.0007 | 49.0 | 61250 | 0.0005 | 0.487 |
| 0.0006 | 50.0 | 62500 | 0.0003 | 0.487 |
| 0.0008 | 51.0 | 63750 | 0.0007 | 0.487 |
| 0.0015 | 52.0 | 65000 | 0.0020 | 0.487 |
| 0.0005 | 53.0 | 66250 | 0.0011 | 0.487 |
| 0.0002 | 54.0 | 67500 | 0.0009 | 0.487 |
| 0.0002 | 55.0 | 68750 | 0.0012 | 0.487 |
| 0.0002 | 56.0 | 70000 | 0.0002 | 0.487 |
| 0.0002 | 57.0 | 71250 | 0.0014 | 0.487 |
| 0.0002 | 58.0 | 72500 | 0.0003 | 0.487 |
| 0.0002 | 59.0 | 73750 | 0.0004 | 0.487 |
| 0.0002 | 60.0 | 75000 | 0.0006 | 0.487 |
| 0.0002 | 61.0 | 76250 | 0.0007 | 0.487 |
| 0.0001 | 62.0 | 77500 | 0.0004 | 0.487 |
| 0.0002 | 63.0 | 78750 | 0.0008 | 0.487 |
| 0.0001 | 64.0 | 80000 | 0.0006 | 0.487 |
| 0.0001 | 65.0 | 81250 | 0.0007 | 0.487 |
| 0.0001 | 66.0 | 82500 | 0.0006 | 0.487 |
| 0.0001 | 67.0 | 83750 | 0.0004 | 0.487 |
| 0.0001 | 68.0 | 85000 | 0.0004 | 0.487 |
| 0.0001 | 69.0 | 86250 | 0.0003 | 0.487 |
| 0.0031 | 70.0 | 87500 | 0.0032 | 0.487 |
| 0.0155 | 71.0 | 88750 | 0.0057 | 0.487 |
| 0.0112 | 72.0 | 90000 | 0.0066 | 0.487 |
| 0.0103 | 73.0 | 91250 | 0.0064 | 0.487 |
| 0.0086 | 74.0 | 92500 | 0.0072 | 0.487 |
| 0.0029 | 75.0 | 93750 | 0.0002 | 0.487 |
| 0.0009 | 76.0 | 95000 | 0.0004 | 0.487 |
| 0.0014 | 77.0 | 96250 | 0.0006 | 0.487 |
| 0.0014 | 78.0 | 97500 | 0.0006 | 0.487 |
| 0.0009 | 79.0 | 98750 | 0.0002 | 0.487 |
| 0.0014 | 80.0 | 100000 | 0.0003 | 0.487 |
| 0.0014 | 81.0 | 101250 | 0.0004 | 0.487 |
| 0.0009 | 82.0 | 102500 | 0.0001 | 0.487 |
| 0.0006 | 83.0 | 103750 | 0.0007 | 0.487 |
| 0.0004 | 84.0 | 105000 | 0.0005 | 0.487 |
| 0.0014 | 85.0 | 106250 | 0.0002 | 0.487 |
| 0.0009 | 86.0 | 107500 | 0.0005 | 0.487 |
| 0.0006 | 87.0 | 108750 | 0.0003 | 0.487 |
| 0.0004 | 88.0 | 110000 | 0.0004 | 0.487 |
| 0.0003 | 89.0 | 111250 | 0.0005 | 0.487 |
| 0.0001 | 90.0 | 112500 | 0.0004 | 0.487 |
| 0.0004 | 91.0 | 113750 | 0.0003 | 0.487 |
| 0.0001 | 92.0 | 115000 | 0.0003 | 0.487 |
| 0.0001 | 93.0 | 116250 | 0.0003 | 0.487 |
| 0.0056 | 94.0 | 117500 | 0.0053 | 0.487 |
| 0.0049 | 95.0 | 118750 | 0.0046 | 0.487 |
| 0.0036 | 96.0 | 120000 | 0.0042 | 0.487 |
| 0.0029 | 97.0 | 121250 | 0.0002 | 0.487 |
| 0.0021 | 98.0 | 122500 | 0.0003 | 0.487 |
| 0.0028 | 99.0 | 123750 | 0.0094 | 0.487 |
| 0.0038 | 100.0 | 125000 | 0.0074 | 0.487 |
| 0.0051 | 101.0 | 126250 | 0.0041 | 0.487 |
| 0.0046 | 102.0 | 127500 | 0.0042 | 0.487 |
| 0.0041 | 103.0 | 128750 | 0.0042 | 0.487 |
| 0.0026 | 104.0 | 130000 | 0.0023 | 0.487 |
| 0.0034 | 105.0 | 131250 | 0.0023 | 0.487 |
| 0.0041 | 106.0 | 132500 | 0.0022 | 0.487 |
| 0.0028 | 107.0 | 133750 | 0.0022 | 0.487 |
| 0.0038 | 108.0 | 135000 | 0.0022 | 0.487 |
| 0.0029 | 109.0 | 136250 | 0.0022 | 0.487 |
| 0.0026 | 110.0 | 137500 | 0.0021 | 0.487 |
| 0.0051 | 111.0 | 138750 | 0.0119 | 0.487 |
| 0.0305 | 112.0 | 140000 | 0.0091 | 0.487 |
| 0.0063 | 113.0 | 141250 | 0.0092 | 0.487 |
| 0.0073 | 114.0 | 142500 | 0.0092 | 0.487 |
| 0.008 | 115.0 | 143750 | 0.0090 | 0.487 |
| 0.0031 | 116.0 | 145000 | 0.0003 | 0.487 |
| 0.0101 | 117.0 | 146250 | 0.0148 | 0.487 |
| 0.0065 | 118.0 | 147500 | 0.0071 | 0.487 |
| 0.0042 | 119.0 | 148750 | 0.0008 | 0.487 |
| 0.0031 | 120.0 | 150000 | 0.0001 | 0.487 |
| 0.0021 | 121.0 | 151250 | 0.0011 | 0.487 |
| 0.0034 | 122.0 | 152500 | 0.0001 | 0.487 |
| 0.0014 | 123.0 | 153750 | 0.0001 | 0.487 |
| 0.0008 | 124.0 | 155000 | 0.0001 | 0.487 |
| 0.0013 | 125.0 | 156250 | 0.0001 | 0.487 |
| 0.0016 | 126.0 | 157500 | 0.0000 | 0.487 |
| 0.0022 | 127.0 | 158750 | 0.0002 | 0.487 |
| 0.0001 | 128.0 | 160000 | 0.0002 | 0.487 |
| 0.0001 | 129.0 | 161250 | 0.0000 | 0.487 |
| 0.0001 | 130.0 | 162500 | 0.0002 | 0.487 |
| 0.0001 | 131.0 | 163750 | 0.0001 | 0.487 |
| 0.0001 | 132.0 | 165000 | 0.0002 | 0.487 |
| 0.0008 | 133.0 | 166250 | 0.0001 | 0.487 |
| 0.0001 | 134.0 | 167500 | 0.0001 | 0.487 |
| 0.0001 | 135.0 | 168750 | 0.0001 | 0.487 |
| 0.0001 | 136.0 | 170000 | 0.0002 | 0.487 |
| 0.0001 | 137.0 | 171250 | 0.0001 | 0.487 |
| 0.0001 | 138.0 | 172500 | 0.0001 | 0.487 |
| 0.0001 | 139.0 | 173750 | 0.0001 | 0.487 |
| 0.0001 | 140.0 | 175000 | 0.0002 | 0.487 |
| 0.0001 | 141.0 | 176250 | 0.0001 | 0.487 |
| 0.0001 | 142.0 | 177500 | 0.0001 | 0.487 |
| 0.0001 | 143.0 | 178750 | 0.0001 | 0.487 |
| 0.0001 | 144.0 | 180000 | 0.0001 | 0.487 |
| 0.0001 | 145.0 | 181250 | 0.0000 | 0.487 |
| 0.0001 | 146.0 | 182500 | 0.0000 | 0.487 |
| 0.0001 | 147.0 | 183750 | 0.0000 | 0.487 |
| 0.0001 | 148.0 | 185000 | 0.0000 | 0.487 |
| 0.0001 | 149.0 | 186250 | 0.0001 | 0.487 |
| 0.0001 | 150.0 | 187500 | 0.0000 | 0.487 |
| 0.0001 | 151.0 | 188750 | 0.0000 | 0.487 |
| 0.0001 | 152.0 | 190000 | 0.0000 | 0.487 |
| 0.0001 | 153.0 | 191250 | 0.0000 | 0.487 |
| 0.0001 | 154.0 | 192500 | 0.0001 | 0.487 |
| 0.0001 | 155.0 | 193750 | 0.0001 | 0.487 |
| 0.0001 | 156.0 | 195000 | 0.0000 | 0.487 |
| 0.0001 | 157.0 | 196250 | 0.0001 | 0.487 |
| 0.0001 | 158.0 | 197500 | 0.0001 | 0.487 |
| 0.0001 | 159.0 | 198750 | 0.0001 | 0.487 |
| 0.0001 | 160.0 | 200000 | 0.0001 | 0.487 |
| 0.0001 | 161.0 | 201250 | 0.0001 | 0.487 |
| 0.0001 | 162.0 | 202500 | 0.0000 | 0.487 |
| 0.0001 | 163.0 | 203750 | 0.0001 | 0.487 |
| 0.0001 | 164.0 | 205000 | 0.0001 | 0.487 |
| 0.0001 | 165.0 | 206250 | 0.0001 | 0.487 |
| 0.0001 | 166.0 | 207500 | 0.0000 | 0.487 |
| 0.0001 | 167.0 | 208750 | 0.0000 | 0.487 |
| 0.0001 | 168.0 | 210000 | 0.0000 | 0.487 |
| 0.0001 | 169.0 | 211250 | 0.0000 | 0.487 |
| 0.0001 | 170.0 | 212500 | 0.0001 | 0.487 |
| 0.0001 | 171.0 | 213750 | 0.0001 | 0.487 |
| 0.0001 | 172.0 | 215000 | 0.0000 | 0.487 |
| 0.0001 | 173.0 | 216250 | 0.0001 | 0.487 |
| 0.0001 | 174.0 | 217500 | 0.0001 | 0.487 |
| 0.0001 | 175.0 | 218750 | 0.0000 | 0.487 |
| 0.0001 | 176.0 | 220000 | 0.0000 | 0.487 |
| 0.0001 | 177.0 | 221250 | 0.0001 | 0.487 |
| 0.0001 | 178.0 | 222500 | 0.0000 | 0.487 |
| 0.0001 | 179.0 | 223750 | 0.0001 | 0.487 |
| 0.0001 | 180.0 | 225000 | 0.0001 | 0.487 |
| 0.0001 | 181.0 | 226250 | 0.0000 | 0.487 |
| 0.0001 | 182.0 | 227500 | 0.0000 | 0.487 |
| 0.0001 | 183.0 | 228750 | 0.0000 | 0.487 |
| 0.0001 | 184.0 | 230000 | 0.0001 | 0.487 |
| 0.0001 | 185.0 | 231250 | 0.0000 | 0.487 |
| 0.0001 | 186.0 | 232500 | 0.0001 | 0.487 |
| 0.0001 | 187.0 | 233750 | 0.0001 | 0.487 |
| 0.0001 | 188.0 | 235000 | 0.0000 | 0.487 |
| 0.0001 | 189.0 | 236250 | 0.0000 | 0.487 |
| 0.0001 | 190.0 | 237500 | 0.0000 | 0.487 |
| 0.0001 | 191.0 | 238750 | 0.0001 | 0.487 |
| 0.0001 | 192.0 | 240000 | 0.0000 | 0.487 |
| 0.0001 | 193.0 | 241250 | 0.0000 | 0.487 |
| 0.0001 | 194.0 | 242500 | 0.0000 | 0.487 |
| 0.0001 | 195.0 | 243750 | 0.0001 | 0.487 |
| 0.0001 | 196.0 | 245000 | 0.0000 | 0.487 |
| 0.0001 | 197.0 | 246250 | 0.0000 | 0.487 |
| 0.0001 | 198.0 | 247500 | 0.0000 | 0.487 |
| 0.0001 | 199.0 | 248750 | 0.0001 | 0.487 |
| 0.0001 | 200.0 | 250000 | 0.0000 | 0.487 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
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