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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: training-5
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. -->
# training-5
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0341
- Accuracy: 0.9952
- Precision: 0.9982
- Recall: 0.9841
- F1: 0.9911
## 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: 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 | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.25 | 151 | 0.0468 | 0.9923 | 1.0 | 0.9717 | 0.9856 |
| No log | 0.5 | 302 | 0.0497 | 0.9908 | 0.9840 | 0.9823 | 0.9832 |
| No log | 0.75 | 453 | 0.0571 | 0.9918 | 1.0 | 0.9699 | 0.9847 |
| No log | 1.0 | 604 | 0.0319 | 0.9961 | 1.0 | 0.9858 | 0.9929 |
| 0.0471 | 1.25 | 755 | 0.0353 | 0.9952 | 0.9982 | 0.9841 | 0.9911 |
| 0.0471 | 1.5 | 906 | 0.0346 | 0.9942 | 0.9929 | 0.9858 | 0.9893 |
| 0.0471 | 1.75 | 1057 | 0.0678 | 0.9899 | 0.9772 | 0.9858 | 0.9815 |
| 0.0471 | 2.0 | 1208 | 0.0380 | 0.9952 | 1.0 | 0.9823 | 0.9911 |
| 0.0156 | 2.25 | 1359 | 0.0362 | 0.9952 | 1.0 | 0.9823 | 0.9911 |
| 0.0156 | 2.5 | 1510 | 0.0388 | 0.9942 | 0.9946 | 0.9841 | 0.9893 |
| 0.0156 | 2.75 | 1661 | 0.0418 | 0.9952 | 1.0 | 0.9823 | 0.9911 |
| 0.0156 | 3.0 | 1812 | 0.0333 | 0.9952 | 0.9982 | 0.9841 | 0.9911 |
| 0.0121 | 3.24 | 1963 | 0.0326 | 0.9952 | 0.9982 | 0.9841 | 0.9911 |
| 0.0121 | 3.49 | 2114 | 0.0309 | 0.9957 | 0.9982 | 0.9858 | 0.9920 |
| 0.0121 | 3.74 | 2265 | 0.0311 | 0.9952 | 0.9982 | 0.9841 | 0.9911 |
| 0.0121 | 3.99 | 2416 | 0.0344 | 0.9952 | 0.9982 | 0.9841 | 0.9911 |
| 0.0084 | 4.24 | 2567 | 0.0334 | 0.9952 | 0.9982 | 0.9841 | 0.9911 |
| 0.0084 | 4.49 | 2718 | 0.0327 | 0.9952 | 0.9982 | 0.9841 | 0.9911 |
| 0.0084 | 4.74 | 2869 | 0.0336 | 0.9952 | 0.9982 | 0.9841 | 0.9911 |
| 0.0084 | 4.99 | 3020 | 0.0341 | 0.9952 | 0.9982 | 0.9841 | 0.9911 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.2.0.dev20230913+cu121
- Tokenizers 0.13.3
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