--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: uwb_atcc results: [] --- # uwb_atcc 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: 0.6191 - Accuracy: 0.9103 - Precision: 0.9239 - Recall: 0.9161 - F1: 0.9200 - Report: precision recall f1-score support 0 0.89 0.90 0.90 463 1 0.92 0.92 0.92 596 accuracy 0.91 1059 macro avg 0.91 0.91 0.91 1059 weighted avg 0.91 0.91 0.91 1059 ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Report | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | No log | 3.36 | 500 | 0.2346 | 0.9207 | 0.9197 | 0.9413 | 0.9303 | precision recall f1-score support 0 0.92 0.89 0.91 463 1 0.92 0.94 0.93 596 accuracy 0.92 1059 macro avg 0.92 0.92 0.92 1059 weighted avg 0.92 0.92 0.92 1059 | | 0.2212 | 6.71 | 1000 | 0.3161 | 0.9046 | 0.9260 | 0.9027 | 0.9142 | precision recall f1-score support 0 0.88 0.91 0.89 463 1 0.93 0.90 0.91 596 accuracy 0.90 1059 macro avg 0.90 0.90 0.90 1059 weighted avg 0.91 0.90 0.90 1059 | | 0.2212 | 10.07 | 1500 | 0.4337 | 0.9065 | 0.9191 | 0.9144 | 0.9167 | precision recall f1-score support 0 0.89 0.90 0.89 463 1 0.92 0.91 0.92 596 accuracy 0.91 1059 macro avg 0.90 0.91 0.91 1059 weighted avg 0.91 0.91 0.91 1059 | | 0.0651 | 13.42 | 2000 | 0.4743 | 0.9178 | 0.9249 | 0.9295 | 0.9272 | precision recall f1-score support 0 0.91 0.90 0.91 463 1 0.92 0.93 0.93 596 accuracy 0.92 1059 macro avg 0.92 0.92 0.92 1059 weighted avg 0.92 0.92 0.92 1059 | | 0.0651 | 16.78 | 2500 | 0.5538 | 0.9103 | 0.9196 | 0.9211 | 0.9204 | precision recall f1-score support 0 0.90 0.90 0.90 463 1 0.92 0.92 0.92 596 accuracy 0.91 1059 macro avg 0.91 0.91 0.91 1059 weighted avg 0.91 0.91 0.91 1059 | | 0.0296 | 20.13 | 3000 | 0.6191 | 0.9103 | 0.9239 | 0.9161 | 0.9200 | precision recall f1-score support 0 0.89 0.90 0.90 463 1 0.92 0.92 0.92 596 accuracy 0.91 1059 macro avg 0.91 0.91 0.91 1059 weighted avg 0.91 0.91 0.91 1059 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.0 - Tokenizers 0.13.2