---
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