| --- |
| library_name: transformers |
| license: mit |
| base_model: microsoft/deberta-v3-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: deberta-4-disfluency |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # deberta-4-disfluency |
|
|
| This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0093 |
| - Precision: 0.9149 |
| - Recall: 0.9306 |
| - F1: 0.9226 |
| - Accuracy: 0.9841 |
| - Disfluent Precision: 0.9533 |
| - Disfluent Recall: 0.9667 |
| - Disfluent F1: 0.9599 |
| - False Positive Rate: 0.0115 |
|
|
| ## 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: 64 |
| - eval_batch_size: 192 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Disfluent Precision | Disfluent Recall | Disfluent F1 | False Positive Rate | |
| |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------------:|:----------------:|:------------:|:-------------------:| |
| | 0.0079 | 0.1821 | 250 | 0.0090 | 0.9100 | 0.9259 | 0.9179 | 0.9833 | 0.9522 | 0.9633 | 0.9577 | 0.0117 | |
| | 0.0077 | 0.3642 | 500 | 0.0098 | 0.9118 | 0.9296 | 0.9206 | 0.9840 | 0.9595 | 0.9592 | 0.9594 | 0.0098 | |
| | 0.0059 | 0.5462 | 750 | 0.0146 | 0.9168 | 0.9287 | 0.9227 | 0.9836 | 0.9654 | 0.9508 | 0.9581 | 0.0083 | |
| | 0.0058 | 0.7283 | 1000 | 0.0109 | 0.9157 | 0.9242 | 0.9199 | 0.9837 | 0.9549 | 0.9623 | 0.9586 | 0.0110 | |
| | 0.0055 | 0.9104 | 1250 | 0.0116 | 0.9131 | 0.9279 | 0.9205 | 0.9834 | 0.9530 | 0.9632 | 0.9581 | 0.0115 | |
| | 0.0072 | 1.0925 | 1500 | 0.0093 | 0.8936 | 0.9201 | 0.9067 | 0.9815 | 0.9396 | 0.9679 | 0.9535 | 0.0150 | |
| | 0.0062 | 1.2746 | 1750 | 0.0103 | 0.9085 | 0.9226 | 0.9155 | 0.9822 | 0.9455 | 0.9652 | 0.9553 | 0.0134 | |
| | 0.0059 | 1.4567 | 2000 | 0.0088 | 0.9079 | 0.9225 | 0.9151 | 0.9816 | 0.9371 | 0.9715 | 0.9540 | 0.0157 | |
| | 0.0062 | 1.6387 | 2250 | 0.0093 | 0.9149 | 0.9306 | 0.9226 | 0.9841 | 0.9533 | 0.9667 | 0.9599 | 0.0115 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.52.4 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 2.14.4 |
| - Tokenizers 0.21.1 |
| |
