--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 - accuracy base_model: bert-base-uncased model-index: - name: final-lr2e-5-bs16-fp16-2 results: [] --- # final-lr2e-5-bs16-fp16-2 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: 0.4823 - F1 Macro: 0.8301 - F1 Weighted: 0.8772 - F1: 0.7388 - Accuracy: 0.8792 - Confusion Matrix: [[2834 196] [ 287 683]] - Confusion Matrix Norm: [[0.93531353 0.06468647] [0.29587629 0.70412371]] - Classification Report: precision recall f1-score support 0 0.908042 0.935314 0.921476 3030.00000 1 0.777019 0.704124 0.738778 970.00000 accuracy 0.879250 0.879250 0.879250 0.87925 macro avg 0.842531 0.819719 0.830127 4000.00000 weighted avg 0.876269 0.879250 0.877172 4000.00000 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | 0.3333 | 1.0 | 1000 | 0.3064 | 0.8165 | 0.8672 | 0.7181 | 0.8692 | [[2811 219] [ 304 666]] | [[0.92772277 0.07227723] [0.31340206 0.68659794]] | precision recall f1-score support 0 0.902408 0.927723 0.914890 3030.00000 1 0.752542 0.686598 0.718059 970.00000 accuracy 0.869250 0.869250 0.869250 0.86925 macro avg 0.827475 0.807160 0.816475 4000.00000 weighted avg 0.866065 0.869250 0.867159 4000.00000 | | 0.2271 | 2.0 | 2000 | 0.3905 | 0.8238 | 0.8708 | 0.7326 | 0.871 | [[2777 253] [ 263 707]] | [[0.91650165 0.08349835] [0.27113402 0.72886598]] | precision recall f1-score support 0 0.913487 0.916502 0.914992 3030.000 1 0.736458 0.728866 0.732642 970.000 accuracy 0.871000 0.871000 0.871000 0.871 macro avg 0.824973 0.822684 0.823817 4000.000 weighted avg 0.870557 0.871000 0.870772 4000.000 | | 0.1435 | 3.0 | 3000 | 0.4823 | 0.8301 | 0.8772 | 0.7388 | 0.8792 | [[2834 196] [ 287 683]] | [[0.93531353 0.06468647] [0.29587629 0.70412371]] | precision recall f1-score support 0 0.908042 0.935314 0.921476 3030.00000 1 0.777019 0.704124 0.738778 970.00000 accuracy 0.879250 0.879250 0.879250 0.87925 macro avg 0.842531 0.819719 0.830127 4000.00000 weighted avg 0.876269 0.879250 0.877172 4000.00000 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2