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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: website_classification
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. -->
# website_classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2292
- Accuracy: 0.9504
- F1: 0.9489
- Precision: 0.9510
- Recall: 0.9504
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.404 | 1.0 | 71 | 1.7840 | 0.8865 | 0.8776 | 0.8785 | 0.8865 |
| 1.295 | 2.0 | 142 | 0.8539 | 0.8972 | 0.8871 | 0.8803 | 0.8972 |
| 0.6186 | 3.0 | 213 | 0.4818 | 0.9326 | 0.9263 | 0.9266 | 0.9326 |
| 0.3103 | 4.0 | 284 | 0.3101 | 0.9397 | 0.9343 | 0.9324 | 0.9397 |
| 0.1618 | 5.0 | 355 | 0.3001 | 0.9291 | 0.9251 | 0.9278 | 0.9291 |
| 0.0893 | 6.0 | 426 | 0.2743 | 0.9291 | 0.9251 | 0.9276 | 0.9291 |
| 0.0547 | 7.0 | 497 | 0.2605 | 0.9255 | 0.9236 | 0.9334 | 0.9255 |
| 0.028 | 8.0 | 568 | 0.2167 | 0.9397 | 0.9375 | 0.9403 | 0.9397 |
| 0.0186 | 9.0 | 639 | 0.2096 | 0.9468 | 0.9467 | 0.9499 | 0.9468 |
| 0.0134 | 10.0 | 710 | 0.2219 | 0.9362 | 0.9354 | 0.9402 | 0.9362 |
| 0.0107 | 11.0 | 781 | 0.2124 | 0.9468 | 0.9466 | 0.9507 | 0.9468 |
| 0.0087 | 12.0 | 852 | 0.2119 | 0.9504 | 0.9497 | 0.9534 | 0.9504 |
| 0.0075 | 13.0 | 923 | 0.2141 | 0.9504 | 0.9497 | 0.9534 | 0.9504 |
| 0.0066 | 14.0 | 994 | 0.2198 | 0.9433 | 0.9415 | 0.9442 | 0.9433 |
| 0.0058 | 15.0 | 1065 | 0.2188 | 0.9468 | 0.9454 | 0.9474 | 0.9468 |
| 0.0052 | 16.0 | 1136 | 0.2181 | 0.9468 | 0.9454 | 0.9474 | 0.9468 |
| 0.0047 | 17.0 | 1207 | 0.2220 | 0.9504 | 0.9489 | 0.9510 | 0.9504 |
| 0.0044 | 18.0 | 1278 | 0.2232 | 0.9504 | 0.9489 | 0.9510 | 0.9504 |
| 0.004 | 19.0 | 1349 | 0.2216 | 0.9539 | 0.9535 | 0.9565 | 0.9539 |
| 0.0037 | 20.0 | 1420 | 0.2251 | 0.9504 | 0.9489 | 0.9510 | 0.9504 |
| 0.0036 | 21.0 | 1491 | 0.2275 | 0.9468 | 0.9451 | 0.9470 | 0.9468 |
| 0.0034 | 22.0 | 1562 | 0.2264 | 0.9539 | 0.9535 | 0.9565 | 0.9539 |
| 0.0032 | 23.0 | 1633 | 0.2283 | 0.9504 | 0.9489 | 0.9510 | 0.9504 |
| 0.003 | 24.0 | 1704 | 0.2299 | 0.9504 | 0.9489 | 0.9510 | 0.9504 |
| 0.0029 | 25.0 | 1775 | 0.2282 | 0.9468 | 0.9451 | 0.9470 | 0.9468 |
| 0.0029 | 26.0 | 1846 | 0.2288 | 0.9468 | 0.9451 | 0.9470 | 0.9468 |
| 0.0028 | 27.0 | 1917 | 0.2286 | 0.9504 | 0.9489 | 0.9510 | 0.9504 |
| 0.0027 | 28.0 | 1988 | 0.2293 | 0.9504 | 0.9489 | 0.9510 | 0.9504 |
| 0.0026 | 29.0 | 2059 | 0.2291 | 0.9504 | 0.9489 | 0.9510 | 0.9504 |
| 0.0026 | 30.0 | 2130 | 0.2292 | 0.9504 | 0.9489 | 0.9510 | 0.9504 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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