| --- |
| license: apache-2.0 |
| base_model: bert-base-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: bert-base-uncased_roberta-base |
| 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-base-uncased_roberta-base |
| |
| 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: 0.4695 |
| - Accuracy: 0.8705 |
| - F1: 0.8700 |
| - Precision: 0.8734 |
| - Recall: 0.8705 |
| |
| ## 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: 0.0001 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 1000 |
| - num_epochs: 25 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.8895 | 1.0 | 91 | 0.8628 | 0.6147 | 0.5774 | 0.5987 | 0.6147 | |
| | 0.5526 | 2.0 | 182 | 0.5921 | 0.7722 | 0.7705 | 0.7856 | 0.7722 | |
| | 0.3669 | 3.0 | 273 | 0.4204 | 0.8346 | 0.8328 | 0.8359 | 0.8346 | |
| | 0.282 | 4.0 | 364 | 0.4526 | 0.8471 | 0.8475 | 0.8487 | 0.8471 | |
| | 0.1444 | 5.0 | 455 | 0.4695 | 0.8705 | 0.8700 | 0.8734 | 0.8705 | |
| | 0.1611 | 6.0 | 546 | 0.5552 | 0.8502 | 0.8503 | 0.8541 | 0.8502 | |
| | 0.0951 | 7.0 | 637 | 0.6573 | 0.8440 | 0.8430 | 0.8457 | 0.8440 | |
| | 0.1256 | 8.0 | 728 | 0.5882 | 0.8393 | 0.8411 | 0.8569 | 0.8393 | |
| | 0.1021 | 9.0 | 819 | 0.5695 | 0.8612 | 0.8614 | 0.8632 | 0.8612 | |
| | 0.0762 | 10.0 | 910 | 0.8848 | 0.8003 | 0.7958 | 0.8109 | 0.8003 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.37.0 |
| - Pytorch 2.1.2 |
| - Datasets 2.1.0 |
| - Tokenizers 0.15.1 |
|
|
