| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: disfluency-large-2 |
| 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. --> |
|
|
| # disfluency-large-2 |
|
|
| This model is a fine-tuned version of [vinai/phobert-large](https://huggingface.co/vinai/phobert-large) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0318 |
| - Precision: 0.9837 |
| - Recall: 0.9808 |
| - F1: 0.9822 |
| - Accuracy: 0.9946 |
|
|
| ## 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: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 50 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 140 | 0.0439 | 0.9538 | 0.9561 | 0.9550 | 0.9890 | |
| | No log | 2.0 | 280 | 0.0314 | 0.9660 | 0.9736 | 0.9698 | 0.9906 | |
| | No log | 3.0 | 420 | 0.0394 | 0.9710 | 0.9651 | 0.9681 | 0.9909 | |
| | 0.1105 | 4.0 | 560 | 0.0320 | 0.9795 | 0.9784 | 0.9790 | 0.9929 | |
| | 0.1105 | 5.0 | 700 | 0.0450 | 0.9704 | 0.9657 | 0.9681 | 0.9904 | |
| | 0.1105 | 6.0 | 840 | 0.0463 | 0.9776 | 0.9694 | 0.9734 | 0.9911 | |
| | 0.1105 | 7.0 | 980 | 0.0480 | 0.9706 | 0.9712 | 0.9709 | 0.9909 | |
| | 0.0113 | 8.0 | 1120 | 0.0318 | 0.9837 | 0.9808 | 0.9822 | 0.9946 | |
| | 0.0113 | 9.0 | 1260 | 0.0419 | 0.9699 | 0.9669 | 0.9684 | 0.9915 | |
| | 0.0113 | 10.0 | 1400 | 0.0458 | 0.9735 | 0.9712 | 0.9723 | 0.9920 | |
| | 0.0051 | 11.0 | 1540 | 0.0309 | 0.9777 | 0.9766 | 0.9771 | 0.9935 | |
| | 0.0051 | 12.0 | 1680 | 0.0232 | 0.9820 | 0.9820 | 0.9820 | 0.9951 | |
| | 0.0051 | 13.0 | 1820 | 0.0344 | 0.9849 | 0.9784 | 0.9816 | 0.9945 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.29.2 |
| - Pytorch 2.0.0+cu118 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
|
|
