| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - recall |
| - precision |
| model-index: |
| - name: cudaTest |
| 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. --> |
|
|
| # cudaTest |
|
|
| 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: 0.6334 |
| - Compute Metrics: : |
| - Accuracy: 0.676 |
| - Balanced Accuracy: 0.4893 |
| - F1 Score: 0.8058 |
| - Recall: 0.9655 |
| - Precision: 0.6914 |
|
|
| ## 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: 64 |
| - seed: 42 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 256 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Compute Metrics | Accuracy | Balanced Accuracy | F1 Score | Recall | Precision | |
| |:-------------:|:-----:|:----:|:---------------:|:---------------:|:--------:|:-----------------:|:--------:|:------:|:---------:| |
| | No log | 1.0 | 2 | 0.6369 | : | 0.688 | 0.5017 | 0.8134 | 0.9770 | 0.6967 | |
| | No log | 2.0 | 4 | 0.6302 | : | 0.684 | 0.5043 | 0.8092 | 0.9626 | 0.6979 | |
| | No log | 3.0 | 6 | 0.6313 | : | 0.69 | 0.4975 | 0.8161 | 0.9885 | 0.6949 | |
| | No log | 4.0 | 8 | 0.6338 | : | 0.668 | 0.4854 | 0.7995 | 0.9511 | 0.6896 | |
| | 0.6818 | 5.0 | 10 | 0.6334 | : | 0.676 | 0.4893 | 0.8058 | 0.9655 | 0.6914 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.26.0 |
| - Pytorch 1.13.1+cu117 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
| |
