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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: 0.0403
- Precision: 0.9904
- Recall: 0.9880
- F1: 0.9892
- Accuracy: 0.9962
## 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 | 280 | 0.0331 | 0.9719 | 0.9754 | 0.9736 | 0.9926 |
| 0.0853 | 2.0 | 560 | 0.0354 | 0.9771 | 0.9736 | 0.9753 | 0.9923 |
| 0.0853 | 3.0 | 840 | 0.0360 | 0.9759 | 0.9754 | 0.9757 | 0.9928 |
| 0.0119 | 4.0 | 1120 | 0.0255 | 0.9850 | 0.9838 | 0.9844 | 0.9948 |
| 0.0119 | 5.0 | 1400 | 0.0300 | 0.9873 | 0.9850 | 0.9862 | 0.9952 |
| 0.0063 | 6.0 | 1680 | 0.0412 | 0.9848 | 0.9742 | 0.9795 | 0.9927 |
| 0.0063 | 7.0 | 1960 | 0.0304 | 0.9844 | 0.9838 | 0.9841 | 0.9952 |
| 0.0039 | 8.0 | 2240 | 0.0344 | 0.9855 | 0.9820 | 0.9837 | 0.9939 |
| 0.004 | 9.0 | 2520 | 0.0522 | 0.9740 | 0.9681 | 0.9711 | 0.9911 |
| 0.004 | 10.0 | 2800 | 0.0305 | 0.9790 | 0.9790 | 0.9790 | 0.9943 |
| 0.0022 | 11.0 | 3080 | 0.0355 | 0.9837 | 0.9820 | 0.9829 | 0.9945 |
| 0.0022 | 12.0 | 3360 | 0.0400 | 0.9795 | 0.9772 | 0.9783 | 0.9935 |
| 0.002 | 13.0 | 3640 | 0.0394 | 0.9826 | 0.9814 | 0.9820 | 0.9943 |
| 0.002 | 14.0 | 3920 | 0.0452 | 0.9795 | 0.9772 | 0.9783 | 0.9930 |
| 0.0015 | 15.0 | 4200 | 0.0405 | 0.9825 | 0.9808 | 0.9817 | 0.9935 |
| 0.0015 | 16.0 | 4480 | 0.0373 | 0.9832 | 0.9826 | 0.9829 | 0.9941 |
| 0.0013 | 17.0 | 4760 | 0.0361 | 0.9832 | 0.9850 | 0.9841 | 0.9946 |
| 0.0013 | 18.0 | 5040 | 0.0447 | 0.9807 | 0.9790 | 0.9798 | 0.9937 |
| 0.0013 | 19.0 | 5320 | 0.0340 | 0.9874 | 0.9856 | 0.9865 | 0.9955 |
| 0.0009 | 20.0 | 5600 | 0.0374 | 0.9873 | 0.9826 | 0.9849 | 0.9948 |
| 0.0009 | 21.0 | 5880 | 0.0410 | 0.9843 | 0.9784 | 0.9813 | 0.9943 |
| 0.0007 | 22.0 | 6160 | 0.0275 | 0.9892 | 0.9862 | 0.9877 | 0.9961 |
| 0.0007 | 23.0 | 6440 | 0.0360 | 0.9891 | 0.9850 | 0.9871 | 0.9960 |
| 0.0011 | 24.0 | 6720 | 0.0323 | 0.9868 | 0.9850 | 0.9859 | 0.9954 |
| 0.0006 | 25.0 | 7000 | 0.0386 | 0.9867 | 0.9820 | 0.9843 | 0.9949 |
| 0.0006 | 26.0 | 7280 | 0.0408 | 0.9819 | 0.9802 | 0.9811 | 0.9940 |
| 0.0005 | 27.0 | 7560 | 0.0357 | 0.9867 | 0.9826 | 0.9846 | 0.9953 |
| 0.0005 | 28.0 | 7840 | 0.0370 | 0.9843 | 0.9820 | 0.9832 | 0.9946 |
| 0.0004 | 29.0 | 8120 | 0.0313 | 0.9880 | 0.9874 | 0.9877 | 0.9960 |
| 0.0004 | 30.0 | 8400 | 0.0363 | 0.9892 | 0.9862 | 0.9877 | 0.9956 |
| 0.0004 | 31.0 | 8680 | 0.0402 | 0.9843 | 0.9826 | 0.9835 | 0.9946 |
| 0.0004 | 32.0 | 8960 | 0.0321 | 0.9868 | 0.9850 | 0.9859 | 0.9956 |
| 0.0004 | 33.0 | 9240 | 0.0362 | 0.9861 | 0.9838 | 0.9850 | 0.9950 |
| 0.0003 | 34.0 | 9520 | 0.0307 | 0.9886 | 0.9880 | 0.9883 | 0.9964 |
| 0.0003 | 35.0 | 9800 | 0.0350 | 0.9880 | 0.9862 | 0.9871 | 0.9956 |
| 0.0001 | 36.0 | 10080 | 0.0343 | 0.9868 | 0.9856 | 0.9862 | 0.9956 |
| 0.0001 | 37.0 | 10360 | 0.0374 | 0.9874 | 0.9856 | 0.9865 | 0.9952 |
| 0.0003 | 38.0 | 10640 | 0.0333 | 0.9874 | 0.9868 | 0.9871 | 0.9957 |
| 0.0003 | 39.0 | 10920 | 0.0331 | 0.9886 | 0.9862 | 0.9874 | 0.9956 |
| 0.0001 | 40.0 | 11200 | 0.0349 | 0.9880 | 0.9868 | 0.9874 | 0.9961 |
| 0.0001 | 41.0 | 11480 | 0.0407 | 0.9880 | 0.9868 | 0.9874 | 0.9958 |
| 0.0001 | 42.0 | 11760 | 0.0389 | 0.9874 | 0.9868 | 0.9871 | 0.9959 |
| 0.0001 | 43.0 | 12040 | 0.0387 | 0.9892 | 0.9874 | 0.9883 | 0.9961 |
| 0.0001 | 44.0 | 12320 | 0.0414 | 0.9886 | 0.9868 | 0.9877 | 0.9959 |
| 0.0001 | 45.0 | 12600 | 0.0386 | 0.9886 | 0.9868 | 0.9877 | 0.9961 |
| 0.0001 | 46.0 | 12880 | 0.0408 | 0.9892 | 0.9874 | 0.9883 | 0.9961 |
| 0.0 | 47.0 | 13160 | 0.0402 | 0.9898 | 0.9880 | 0.9889 | 0.9962 |
| 0.0 | 48.0 | 13440 | 0.0411 | 0.9886 | 0.9868 | 0.9877 | 0.9959 |
| 0.0 | 49.0 | 13720 | 0.0403 | 0.9904 | 0.9880 | 0.9892 | 0.9962 |
| 0.0 | 50.0 | 14000 | 0.0402 | 0.9904 | 0.9880 | 0.9892 | 0.9962 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
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