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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: 0.8335 |
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- Precision: 0.8310 |
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- Recall: 0.8213 |
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- F1: 0.8256 |
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- Accuracy: 0.8640 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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| No log | 1.0 | 255 | 0.6597 | 0.7225 | 0.7990 | 0.7429 | 0.7968 | |
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| 0.8033 | 2.0 | 510 | 0.5609 | 0.8155 | 0.8378 | 0.8247 | 0.8596 | |
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| 0.8033 | 3.0 | 765 | 0.5589 | 0.8119 | 0.8388 | 0.8231 | 0.8591 | |
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| 0.2454 | 4.0 | 1020 | 0.6598 | 0.8314 | 0.8273 | 0.8279 | 0.8625 | |
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| 0.2454 | 5.0 | 1275 | 0.6541 | 0.8103 | 0.8393 | 0.8229 | 0.8625 | |
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| 0.1332 | 6.0 | 1530 | 0.8259 | 0.8424 | 0.8213 | 0.8304 | 0.8665 | |
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| 0.1332 | 7.0 | 1785 | 0.7644 | 0.8298 | 0.8335 | 0.8312 | 0.8650 | |
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| 0.0907 | 8.0 | 2040 | 0.7939 | 0.8298 | 0.8255 | 0.8274 | 0.8660 | |
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| 0.0907 | 9.0 | 2295 | 0.8244 | 0.8310 | 0.8207 | 0.8255 | 0.8655 | |
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| 0.061 | 10.0 | 2550 | 0.8335 | 0.8310 | 0.8213 | 0.8256 | 0.8640 | |
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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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