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---
base_model: vinai/phobert-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: disfluency-large-3
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-3
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 42.3155
- Precision: 0.9886
- Recall: 0.9862
- F1: 0.9874
- Accuracy: 0.9956
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 140 | 131.9614 | 0.8037 | 0.8438 | 0.8232 | 0.9411 |
| No log | 2.0 | 280 | 41.8031 | 0.9487 | 0.9549 | 0.9518 | 0.9855 |
| No log | 3.0 | 420 | 27.3502 | 0.9664 | 0.9681 | 0.9673 | 0.9906 |
| 178.6738 | 4.0 | 560 | 22.9255 | 0.9741 | 0.9730 | 0.9735 | 0.9925 |
| 178.6738 | 5.0 | 700 | 25.3163 | 0.9676 | 0.9688 | 0.9682 | 0.9919 |
| 178.6738 | 6.0 | 840 | 24.1142 | 0.9723 | 0.9718 | 0.9720 | 0.9925 |
| 178.6738 | 7.0 | 980 | 22.2517 | 0.9777 | 0.9766 | 0.9771 | 0.9938 |
| 25.7318 | 8.0 | 1120 | 24.4542 | 0.9760 | 0.9772 | 0.9766 | 0.9936 |
| 25.7318 | 9.0 | 1260 | 27.1333 | 0.9740 | 0.9700 | 0.9720 | 0.9929 |
| 25.7318 | 10.0 | 1400 | 24.5889 | 0.9789 | 0.9778 | 0.9784 | 0.9938 |
| 16.0059 | 11.0 | 1540 | 26.1038 | 0.9819 | 0.9808 | 0.9814 | 0.9936 |
| 16.0059 | 12.0 | 1680 | 23.3198 | 0.9790 | 0.9814 | 0.9802 | 0.9941 |
| 16.0059 | 13.0 | 1820 | 30.8831 | 0.9778 | 0.9772 | 0.9775 | 0.9930 |
| 16.0059 | 14.0 | 1960 | 28.1502 | 0.9843 | 0.9814 | 0.9828 | 0.9946 |
| 11.2302 | 15.0 | 2100 | 29.2842 | 0.9790 | 0.9808 | 0.9799 | 0.9937 |
| 11.2302 | 16.0 | 2240 | 28.5446 | 0.9819 | 0.9796 | 0.9807 | 0.9945 |
| 11.2302 | 17.0 | 2380 | 25.4603 | 0.9850 | 0.9838 | 0.9844 | 0.9953 |
| 8.9848 | 18.0 | 2520 | 29.3936 | 0.9801 | 0.9760 | 0.9780 | 0.9929 |
| 8.9848 | 19.0 | 2660 | 31.2320 | 0.9796 | 0.9796 | 0.9796 | 0.9944 |
| 8.9848 | 20.0 | 2800 | 34.0474 | 0.9849 | 0.9802 | 0.9825 | 0.9943 |
| 8.9848 | 21.0 | 2940 | 32.9968 | 0.9849 | 0.9826 | 0.9838 | 0.9948 |
| 8.2401 | 22.0 | 3080 | 39.6873 | 0.9819 | 0.9808 | 0.9814 | 0.9946 |
| 8.2401 | 23.0 | 3220 | 42.7506 | 0.9819 | 0.9802 | 0.9811 | 0.9945 |
| 8.2401 | 24.0 | 3360 | 33.8886 | 0.9856 | 0.9862 | 0.9859 | 0.9954 |
| 7.099 | 25.0 | 3500 | 36.8275 | 0.9819 | 0.9808 | 0.9814 | 0.9941 |
| 7.099 | 26.0 | 3640 | 36.7838 | 0.9831 | 0.9814 | 0.9823 | 0.9951 |
| 7.099 | 27.0 | 3780 | 39.2226 | 0.9813 | 0.9790 | 0.9801 | 0.9947 |
| 7.099 | 28.0 | 3920 | 39.2492 | 0.9843 | 0.9820 | 0.9832 | 0.9949 |
| 5.6646 | 29.0 | 4060 | 41.4139 | 0.9790 | 0.9790 | 0.9790 | 0.9944 |
| 5.6646 | 30.0 | 4200 | 41.4583 | 0.9838 | 0.9826 | 0.9832 | 0.9949 |
| 5.6646 | 31.0 | 4340 | 47.1872 | 0.9801 | 0.9778 | 0.9789 | 0.9941 |
| 5.6646 | 32.0 | 4480 | 41.3073 | 0.9862 | 0.9844 | 0.9853 | 0.9956 |
| 5.304 | 33.0 | 4620 | 44.8882 | 0.9796 | 0.9790 | 0.9793 | 0.9945 |
| 5.304 | 34.0 | 4760 | 52.3203 | 0.9783 | 0.9772 | 0.9778 | 0.9941 |
| 5.304 | 35.0 | 4900 | 43.9140 | 0.9825 | 0.9808 | 0.9817 | 0.9951 |
| 4.7574 | 36.0 | 5040 | 46.8215 | 0.9819 | 0.9802 | 0.9811 | 0.9947 |
| 4.7574 | 37.0 | 5180 | 39.5738 | 0.9867 | 0.9844 | 0.9856 | 0.9959 |
| 4.7574 | 38.0 | 5320 | 39.9370 | 0.9837 | 0.9814 | 0.9826 | 0.9955 |
| 4.7574 | 39.0 | 5460 | 40.4614 | 0.9856 | 0.9844 | 0.9850 | 0.9956 |
| 3.8125 | 40.0 | 5600 | 38.6418 | 0.9885 | 0.9850 | 0.9868 | 0.9959 |
| 3.8125 | 41.0 | 5740 | 42.7438 | 0.9813 | 0.9796 | 0.9805 | 0.9947 |
| 3.8125 | 42.0 | 5880 | 52.7676 | 0.9689 | 0.9730 | 0.9709 | 0.9940 |
| 3.2902 | 43.0 | 6020 | 38.5737 | 0.9825 | 0.9808 | 0.9817 | 0.9953 |
| 3.2902 | 44.0 | 6160 | 42.4615 | 0.9868 | 0.9850 | 0.9859 | 0.9952 |
| 3.2902 | 45.0 | 6300 | 43.5099 | 0.9856 | 0.9838 | 0.9847 | 0.9956 |
| 3.2902 | 46.0 | 6440 | 45.0846 | 0.9837 | 0.9820 | 0.9829 | 0.9952 |
| 4.0467 | 47.0 | 6580 | 41.7571 | 0.9862 | 0.9850 | 0.9856 | 0.9955 |
| 4.0467 | 48.0 | 6720 | 50.8592 | 0.9807 | 0.9778 | 0.9792 | 0.9945 |
| 4.0467 | 49.0 | 6860 | 42.3155 | 0.9886 | 0.9862 | 0.9874 | 0.9956 |
| 2.3503 | 50.0 | 7000 | 45.7602 | 0.9873 | 0.9850 | 0.9862 | 0.9952 |
| 2.3503 | 51.0 | 7140 | 43.4314 | 0.9856 | 0.9838 | 0.9847 | 0.9953 |
| 2.3503 | 52.0 | 7280 | 47.4167 | 0.9813 | 0.9790 | 0.9801 | 0.9949 |
| 2.3503 | 53.0 | 7420 | 46.8868 | 0.9838 | 0.9826 | 0.9832 | 0.9952 |
| 2.841 | 54.0 | 7560 | 50.8428 | 0.9843 | 0.9814 | 0.9828 | 0.9950 |
| 2.841 | 55.0 | 7700 | 49.0097 | 0.9825 | 0.9808 | 0.9817 | 0.9949 |
| 2.841 | 56.0 | 7840 | 49.0165 | 0.9831 | 0.9802 | 0.9816 | 0.9950 |
| 2.841 | 57.0 | 7980 | 46.3213 | 0.9838 | 0.9826 | 0.9832 | 0.9953 |
| 1.8064 | 58.0 | 8120 | 49.3268 | 0.9825 | 0.9790 | 0.9807 | 0.9946 |
| 1.8064 | 59.0 | 8260 | 48.1988 | 0.9849 | 0.9814 | 0.9831 | 0.9952 |
| 1.8064 | 60.0 | 8400 | 46.5527 | 0.9838 | 0.9826 | 0.9832 | 0.9955 |
| 1.5941 | 61.0 | 8540 | 57.5747 | 0.9807 | 0.9790 | 0.9798 | 0.9942 |
| 1.5941 | 62.0 | 8680 | 56.6894 | 0.9801 | 0.9790 | 0.9796 | 0.9945 |
| 1.5941 | 63.0 | 8820 | 58.1243 | 0.9808 | 0.9802 | 0.9805 | 0.9945 |
| 1.5941 | 64.0 | 8960 | 53.2165 | 0.9837 | 0.9808 | 0.9822 | 0.9951 |
| 1.5057 | 65.0 | 9100 | 52.2484 | 0.9832 | 0.9820 | 0.9826 | 0.9949 |
| 1.5057 | 66.0 | 9240 | 49.2435 | 0.9837 | 0.9814 | 0.9826 | 0.9951 |
| 1.5057 | 67.0 | 9380 | 51.2186 | 0.9796 | 0.9790 | 0.9793 | 0.9948 |
| 1.5084 | 68.0 | 9520 | 54.1799 | 0.9825 | 0.9808 | 0.9817 | 0.9947 |
| 1.5084 | 69.0 | 9660 | 56.3696 | 0.9807 | 0.9778 | 0.9792 | 0.9945 |
| 1.5084 | 70.0 | 9800 | 52.6295 | 0.9837 | 0.9802 | 0.9819 | 0.9948 |
| 1.5084 | 71.0 | 9940 | 51.2577 | 0.9825 | 0.9790 | 0.9807 | 0.9950 |
| 1.0448 | 72.0 | 10080 | 56.0093 | 0.9807 | 0.9790 | 0.9798 | 0.9945 |
| 1.0448 | 73.0 | 10220 | 50.7540 | 0.9831 | 0.9808 | 0.9819 | 0.9951 |
| 1.0448 | 74.0 | 10360 | 52.9783 | 0.9819 | 0.9790 | 0.9804 | 0.9947 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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