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---
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license: apache-2.0
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base_model: bert-base-uncased
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: BERT_with_preprocessing_grid_search
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# BERT_with_preprocessing_grid_search
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9805
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- Precision: 0.8330
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- Recall: 0.8192
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- F1: 0.8255
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- Accuracy: 0.8630
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.9875 | 1.0 | 510 | 0.5592 | 0.7906 | 0.8234 | 0.8042 | 0.8463 |
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| 0.4428 | 2.0 | 1020 | 0.6291 | 0.8177 | 0.8243 | 0.8198 | 0.8606 |
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| 0.2998 | 3.0 | 1530 | 0.6034 | 0.8133 | 0.8342 | 0.8209 | 0.8586 |
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| 0.2088 | 4.0 | 2040 | 0.7925 | 0.8365 | 0.8152 | 0.8231 | 0.8562 |
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| 0.1563 | 5.0 | 2550 | 0.7368 | 0.8096 | 0.8301 | 0.8190 | 0.8571 |
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| 0.1302 | 6.0 | 3060 | 0.9748 | 0.8449 | 0.8036 | 0.8208 | 0.8591 |
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| 0.1024 | 7.0 | 3570 | 0.9088 | 0.8364 | 0.8264 | 0.8300 | 0.8645 |
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| 0.0897 | 8.0 | 4080 | 0.9440 | 0.8340 | 0.8254 | 0.8283 | 0.8635 |
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| 0.067 | 9.0 | 4590 | 0.9582 | 0.8370 | 0.8169 | 0.8260 | 0.8635 |
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| 0.0526 | 10.0 | 5100 | 0.9805 | 0.8330 | 0.8192 | 0.8255 | 0.8630 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.4
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- Tokenizers 0.13.3
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