| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - accuracy |
| - f1 |
| model-index: |
| - name: Bert_Test |
| 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. --> |
|
|
| # Bert_Test |
| |
| This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1965 |
| - Precision: 0.9332 |
| - Accuracy: 0.9223 |
| - F1: 0.9223 |
| |
| ## 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: 32 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 7 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Accuracy | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:------:| |
| | 0.6717 | 0.4 | 500 | 0.6049 | 0.7711 | 0.6743 | 0.6112 | |
| | 0.5704 | 0.8 | 1000 | 0.5299 | 0.7664 | 0.7187 | 0.6964 | |
| | 0.52 | 1.2 | 1500 | 0.4866 | 0.7698 | 0.7537 | 0.7503 | |
| | 0.4792 | 1.6 | 2000 | 0.4292 | 0.8031 | 0.793 | 0.7927 | |
| | 0.4332 | 2.0 | 2500 | 0.3920 | 0.8318 | 0.8203 | 0.8198 | |
| | 0.381 | 2.4 | 3000 | 0.3723 | 0.9023 | 0.8267 | 0.8113 | |
| | 0.3625 | 2.8 | 3500 | 0.3134 | 0.8736 | 0.8607 | 0.8601 | |
| | 0.3325 | 3.2 | 4000 | 0.2924 | 0.8973 | 0.871 | 0.8683 | |
| | 0.3069 | 3.6 | 4500 | 0.2671 | 0.8916 | 0.8847 | 0.8851 | |
| | 0.2866 | 4.0 | 5000 | 0.2571 | 0.8920 | 0.8913 | 0.8926 | |
| | 0.2595 | 4.4 | 5500 | 0.2450 | 0.8980 | 0.9 | 0.9015 | |
| | 0.2567 | 4.8 | 6000 | 0.2246 | 0.9057 | 0.9043 | 0.9054 | |
| | 0.2255 | 5.2 | 6500 | 0.2263 | 0.9332 | 0.905 | 0.9030 | |
| | 0.2237 | 5.6 | 7000 | 0.2083 | 0.9265 | 0.9157 | 0.9156 | |
| | 0.2248 | 6.0 | 7500 | 0.2039 | 0.9387 | 0.9193 | 0.9185 | |
| | 0.2086 | 6.4 | 8000 | 0.2038 | 0.9436 | 0.9193 | 0.9181 | |
| | 0.2029 | 6.8 | 8500 | 0.1965 | 0.9332 | 0.9223 | 0.9223 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.18.0 |
| - Pytorch 1.10.0+cu111 |
| - Datasets 2.0.0 |
| - Tokenizers 0.11.6 |
|
|
