| --- |
| license: mit |
| tags: |
| - generated_from_trainer |
| datasets: |
| - ag_news |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: results |
| results: |
| - task: |
| name: Text Classification |
| type: text-classification |
| dataset: |
| name: ag_news |
| type: ag_news |
| config: default |
| split: train[:40000] |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.8951 |
| - name: F1 |
| type: f1 |
| value: 0.8964447542636089 |
| - name: Precision |
| type: precision |
| value: 0.8978261707981314 |
| - name: Recall |
| type: recall |
| value: 0.896474840596734 |
| --- |
| |
| <!-- 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 |
|
|
| This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the ag_news dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3320 |
| - Accuracy: 0.8951 |
| - F1: 0.8964 |
| - Precision: 0.8978 |
| - Recall: 0.8965 |
| |
| ## 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: 0.0003 |
| - 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 |
| - num_epochs: 3 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.2783 | 1.0 | 625 | 0.3046 | 0.8949 | 0.8960 | 0.8970 | 0.8963 | |
| | 0.1878 | 2.0 | 1250 | 0.3139 | 0.8954 | 0.8971 | 0.8995 | 0.8965 | |
| | 0.1311 | 3.0 | 1875 | 0.3320 | 0.8951 | 0.8964 | 0.8978 | 0.8965 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.22.0 |
| - Pytorch 1.12.1+cu113 |
| - Datasets 2.4.0 |
| - Tokenizers 0.12.1 |
| |
