| --- |
| license: apache-2.0 |
| base_model: bert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: results_bert-base-uncased |
| 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. --> |
|
|
| # results_bert-base-uncased |
| |
| 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: 0.1808 |
| - Accuracy: 0.9261 |
| - Precision: 0.9343 |
| - Recall: 0.9443 |
| - F1: 0.9393 |
| |
| ## 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: 5e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.5507 | 0.09 | 50 | 0.3325 | 0.8642 | 0.9461 | 0.8224 | 0.8799 | |
| | 0.3193 | 0.18 | 100 | 0.3217 | 0.8701 | 0.9665 | 0.8135 | 0.8835 | |
| | 0.2726 | 0.28 | 150 | 0.2532 | 0.8918 | 0.9091 | 0.9125 | 0.9108 | |
| | 0.2143 | 0.37 | 200 | 0.2203 | 0.9146 | 0.9281 | 0.9310 | 0.9295 | |
| | 0.2134 | 0.46 | 250 | 0.2371 | 0.9162 | 0.9035 | 0.9645 | 0.9330 | |
| | 0.2234 | 0.55 | 300 | 0.2027 | 0.9178 | 0.9130 | 0.9552 | 0.9336 | |
| | 0.2139 | 0.64 | 350 | 0.1986 | 0.9194 | 0.9125 | 0.9587 | 0.9351 | |
| | 0.2062 | 0.74 | 400 | 0.1853 | 0.9222 | 0.9469 | 0.9232 | 0.9349 | |
| | 0.1793 | 0.83 | 450 | 0.1953 | 0.9244 | 0.9213 | 0.9567 | 0.9387 | |
| | 0.1771 | 0.92 | 500 | 0.1808 | 0.9261 | 0.9343 | 0.9443 | 0.9393 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.37.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.17.0 |
| - Tokenizers 0.15.2 |
|
|
