--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: disfluency-4-deberta-v2 results: [] --- # disfluency-4-deberta-v2 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0117 - Model Preparation Time: 0.0032 - Accuracy: 0.9627 - Balanced Accuracy: 0.9358 - F1: 0.9037 - F1 Macro: 0.9403 - Precision: 0.9162 - Precision Macro: 0.9449 - Recall: 0.8917 - Recall Macro: 0.9358 - Specificity: 0.9800 - Sensitivity: 0.8917 - False Positive Rate: 0.0200 - False Negative Rate: 0.1083 - Mcc: 0.8807 - True Positives: 7999 - False Positives: 732 - False Negatives: 972 - True Negatives: 35943 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Accuracy | Balanced Accuracy | F1 | F1 Macro | Precision | Precision Macro | Recall | Recall Macro | Specificity | Sensitivity | False Positive Rate | False Negative Rate | Mcc | True Positives | False Positives | False Negatives | True Negatives | |:-------------:|:------:|:----:|:---------------:|:----------------------:|:--------:|:-----------------:|:------:|:--------:|:---------:|:---------------:|:------:|:------------:|:-----------:|:-----------:|:-------------------:|:-------------------:|:------:|:--------------:|:---------------:|:---------------:|:--------------:| | 0.0245 | 0.2308 | 200 | 0.0237 | 0.0032 | 0.8388 | 0.8286 | 0.6644 | 0.7792 | 0.5624 | 0.7554 | 0.8118 | 0.8286 | 0.8455 | 0.8118 | 0.1545 | 0.1882 | 0.5794 | 7283 | 5668 | 1688 | 31007 | | 0.0179 | 0.4616 | 400 | 0.0173 | 0.0032 | 0.9142 | 0.8860 | 0.7936 | 0.8697 | 0.7525 | 0.8560 | 0.8395 | 0.8860 | 0.9325 | 0.8395 | 0.0675 | 0.1605 | 0.7414 | 7531 | 2477 | 1440 | 34198 | | 0.0153 | 0.6924 | 600 | 0.0149 | 0.0032 | 0.9362 | 0.9114 | 0.8428 | 0.9014 | 0.8167 | 0.8923 | 0.8707 | 0.9114 | 0.9522 | 0.8707 | 0.0478 | 0.1293 | 0.8035 | 7811 | 1753 | 1160 | 34922 | | 0.0131 | 0.9233 | 800 | 0.0135 | 0.0032 | 0.9410 | 0.9208 | 0.8554 | 0.9092 | 0.8257 | 0.8988 | 0.8874 | 0.9208 | 0.9542 | 0.8874 | 0.0458 | 0.1126 | 0.8193 | 7961 | 1681 | 1010 | 34994 | | 0.0121 | 1.1535 | 1000 | 0.0128 | 0.0032 | 0.9574 | 0.9264 | 0.8898 | 0.9317 | 0.9046 | 0.9372 | 0.8754 | 0.9264 | 0.9774 | 0.8754 | 0.0226 | 0.1246 | 0.8635 | 7853 | 828 | 1118 | 35847 | | 0.0115 | 1.3843 | 1200 | 0.0120 | 0.0032 | 0.9549 | 0.9324 | 0.8864 | 0.9291 | 0.8777 | 0.9260 | 0.8952 | 0.9324 | 0.9695 | 0.8952 | 0.0305 | 0.1048 | 0.8583 | 8031 | 1119 | 940 | 35556 | | 0.0102 | 1.6151 | 1400 | 0.0118 | 0.0032 | 0.9605 | 0.9341 | 0.8987 | 0.9371 | 0.9071 | 0.9402 | 0.8904 | 0.9341 | 0.9777 | 0.8904 | 0.0223 | 0.1096 | 0.8743 | 7988 | 818 | 983 | 35857 | | 0.0102 | 1.8459 | 1600 | 0.0117 | 0.0032 | 0.9627 | 0.9358 | 0.9037 | 0.9403 | 0.9162 | 0.9449 | 0.8917 | 0.9358 | 0.9800 | 0.8917 | 0.0200 | 0.1083 | 0.8807 | 7999 | 732 | 972 | 35943 | | 0.0085 | 2.0762 | 1800 | 0.0121 | 0.0032 | 0.9607 | 0.9386 | 0.9003 | 0.9379 | 0.8986 | 0.9373 | 0.9020 | 0.9386 | 0.9751 | 0.9020 | 0.0249 | 0.0980 | 0.8759 | 8092 | 913 | 879 | 35762 | | 0.0092 | 2.3070 | 2000 | 0.0113 | 0.0032 | 0.9613 | 0.9400 | 0.9019 | 0.9389 | 0.8990 | 0.9379 | 0.9049 | 0.9400 | 0.9751 | 0.9049 | 0.0249 | 0.0951 | 0.8779 | 8118 | 912 | 853 | 35763 | | 0.0085 | 2.5378 | 2200 | 0.0115 | 0.0032 | 0.9617 | 0.9411 | 0.9030 | 0.9396 | 0.8989 | 0.9381 | 0.9071 | 0.9411 | 0.9751 | 0.9071 | 0.0249 | 0.0929 | 0.8792 | 8138 | 915 | 833 | 35760 | | 0.0083 | 2.7686 | 2400 | 0.0111 | 0.0032 | 0.9613 | 0.9418 | 0.9024 | 0.9391 | 0.8952 | 0.9365 | 0.9096 | 0.9418 | 0.9740 | 0.9096 | 0.0260 | 0.0904 | 0.8783 | 8160 | 955 | 811 | 35720 | | 0.0083 | 2.9994 | 2600 | 0.0111 | 0.0032 | 0.9618 | 0.9421 | 0.9036 | 0.9399 | 0.8975 | 0.9377 | 0.9097 | 0.9421 | 0.9746 | 0.9097 | 0.0254 | 0.0903 | 0.8798 | 8161 | 932 | 810 | 35743 | ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1