--- tags: - generated_from_trainer metrics: - f1 - accuracy base_model: bert-base-uncased model-index: - name: final-lr2e-5-bs16-fp16-2 results: [] language: - en library_name: transformers pipeline_tag: text-classification --- # final-lr2e-5-bs16-fp16-2 This model is a fine-tuned version of [GroNLP/hateBERT](https://huggingface.co./GroNLP/hateBERT) on an https://github.com/rewire-online/edos dataset. It achieves the following results on the evaluation set: - Loss: 0.4219 - F1 Macro: 0.8457 - F1 Weighted: 0.8868 - F1: 0.7658 - Accuracy: 0.887 - Confusion Matrix: [[2809 221] [ 231 739]] - Confusion Matrix Norm: [[0.92706271 0.07293729] [0.23814433 0.76185567]] - Classification Report: precision recall f1-score support 0 0.924013 0.927063 0.925535 3030.000 1 0.769792 0.761856 0.765803 970.000 accuracy 0.887000 0.887000 0.887000 0.887 macro avg 0.846902 0.844459 0.845669 4000.000 weighted avg 0.886614 0.887000 0.886800 4000.000 ## 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: 16 - eval_batch_size: 16 - seed: 12345 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.3177 | 1.0 | 1000 | 0.2894 | 0.8323 | 0.8812 | 0.7373 | 0.886 | [[2904 126] [ 330 640]] | [[0.95841584 0.04158416] [0.34020619 0.65979381]] | precision recall f1-score support 0 0.897959 0.958416 0.927203 3030.000 1 0.835509 0.659794 0.737327 970.000 accuracy 0.886000 0.886000 0.886000 0.886 macro avg 0.866734 0.809105 0.832265 4000.000 weighted avg 0.882815 0.886000 0.881158 4000.000 | | 0.2232 | 2.0 | 2000 | 0.3370 | 0.8405 | 0.8830 | 0.7579 | 0.8832 | [[2802 228] [ 239 731]] | [[0.92475248 0.07524752] [0.24639175 0.75360825]] | precision recall f1-score support 0 0.921407 0.924752 0.923077 3030.00000 1 0.762252 0.753608 0.757906 970.00000 accuracy 0.883250 0.883250 0.883250 0.88325 macro avg 0.841830 0.839180 0.840491 4000.00000 weighted avg 0.882812 0.883250 0.883023 4000.00000 | | 0.1534 | 3.0 | 3000 | 0.4219 | 0.8457 | 0.8868 | 0.7658 | 0.887 | [[2809 221] [ 231 739]] | [[0.92706271 0.07293729] [0.23814433 0.76185567]] | precision recall f1-score support 0 0.924013 0.927063 0.925535 3030.000 1 0.769792 0.761856 0.765803 970.000 accuracy 0.887000 0.887000 0.887000 0.887 macro avg 0.846902 0.844459 0.845669 4000.000 weighted avg 0.886614 0.887000 0.886800 4000.000 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2