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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: hubert-large-korean-finetuned-korspeech-ser2
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. -->
# hubert-large-korean-finetuned-korspeech-ser2
This model is a fine-tuned version of [team-lucid/hubert-large-korean](https://huggingface.co/team-lucid/hubert-large-korean) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4456
- Macro F1: 0.6275
- Accuracy: 0.6278
- Weighted f1: 0.6275
- Micro f1: 0.6278
- Weighted recall: 0.6278
- Micro recall: 0.6278
- Macro recall: 0.6278
- Weighted precision: 0.6296
- Micro precision: 0.6278
- Macro precision: 0.6296
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 100
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Accuracy | Weighted f1 | Micro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.3419 | 0.28 | 100 | 1.2665 | 0.4034 | 0.4276 | 0.4034 | 0.4276 | 0.4276 | 0.4276 | 0.4276 | 0.4520 | 0.4276 | 0.4520 |
| 1.2338 | 0.57 | 200 | 1.1790 | 0.4459 | 0.4638 | 0.4459 | 0.4638 | 0.4638 | 0.4638 | 0.4638 | 0.4618 | 0.4638 | 0.4618 |
| 1.1842 | 0.85 | 300 | 1.1540 | 0.4560 | 0.4972 | 0.4560 | 0.4972 | 0.4972 | 0.4972 | 0.4972 | 0.4966 | 0.4972 | 0.4966 |
| 1.1304 | 1.13 | 400 | 1.1268 | 0.5056 | 0.5249 | 0.5056 | 0.5249 | 0.5249 | 0.5249 | 0.5249 | 0.5269 | 0.5249 | 0.5269 |
| 1.0815 | 1.42 | 500 | 1.0745 | 0.5390 | 0.5447 | 0.5390 | 0.5447 | 0.5447 | 0.5447 | 0.5447 | 0.5396 | 0.5447 | 0.5396 |
| 1.0822 | 1.7 | 600 | 1.0715 | 0.5071 | 0.5270 | 0.5071 | 0.5270 | 0.5270 | 0.5270 | 0.5270 | 0.5260 | 0.5270 | 0.5260 |
| 1.0484 | 1.99 | 700 | 1.0213 | 0.5555 | 0.5632 | 0.5555 | 0.5632 | 0.5632 | 0.5632 | 0.5632 | 0.5573 | 0.5632 | 0.5573 |
| 0.9784 | 2.27 | 800 | 1.0601 | 0.5640 | 0.5739 | 0.5640 | 0.5739 | 0.5739 | 0.5739 | 0.5739 | 0.5715 | 0.5739 | 0.5715 |
| 0.9627 | 2.55 | 900 | 1.0287 | 0.5606 | 0.5746 | 0.5606 | 0.5746 | 0.5746 | 0.5746 | 0.5746 | 0.5714 | 0.5746 | 0.5714 |
| 0.9614 | 2.84 | 1000 | 0.9945 | 0.5705 | 0.5753 | 0.5705 | 0.5753 | 0.5753 | 0.5753 | 0.5753 | 0.5782 | 0.5753 | 0.5782 |
| 0.9379 | 3.12 | 1100 | 1.0166 | 0.5852 | 0.5881 | 0.5852 | 0.5881 | 0.5881 | 0.5881 | 0.5881 | 0.5899 | 0.5881 | 0.5899 |
| 0.8982 | 3.4 | 1200 | 1.0289 | 0.5685 | 0.5724 | 0.5685 | 0.5724 | 0.5724 | 0.5724 | 0.5724 | 0.5905 | 0.5724 | 0.5905 |
| 0.8651 | 3.69 | 1300 | 1.0100 | 0.5967 | 0.6001 | 0.5967 | 0.6001 | 0.6001 | 0.6001 | 0.6001 | 0.6005 | 0.6001 | 0.6005 |
| 0.9017 | 3.97 | 1400 | 1.0405 | 0.5702 | 0.5739 | 0.5702 | 0.5739 | 0.5739 | 0.5739 | 0.5739 | 0.5884 | 0.5739 | 0.5884 |
| 0.8152 | 4.26 | 1500 | 0.9874 | 0.6016 | 0.6030 | 0.6016 | 0.6030 | 0.6030 | 0.6030 | 0.6030 | 0.6090 | 0.6030 | 0.6090 |
| 0.8149 | 4.54 | 1600 | 0.9994 | 0.6001 | 0.6044 | 0.6001 | 0.6044 | 0.6044 | 0.6044 | 0.6044 | 0.6092 | 0.6044 | 0.6092 |
| 0.7978 | 4.82 | 1700 | 1.0319 | 0.5945 | 0.6080 | 0.5945 | 0.6080 | 0.6080 | 0.6080 | 0.6080 | 0.6093 | 0.6080 | 0.6093 |
| 0.7674 | 5.11 | 1800 | 1.0800 | 0.5884 | 0.5909 | 0.5884 | 0.5909 | 0.5909 | 0.5909 | 0.5909 | 0.6128 | 0.5909 | 0.6128 |
| 0.7126 | 5.39 | 1900 | 1.0071 | 0.6177 | 0.6200 | 0.6177 | 0.6200 | 0.6200 | 0.6200 | 0.6200 | 0.6229 | 0.6200 | 0.6229 |
| 0.7229 | 5.67 | 2000 | 1.0267 | 0.6141 | 0.6165 | 0.6141 | 0.6165 | 0.6165 | 0.6165 | 0.6165 | 0.6141 | 0.6165 | 0.6141 |
| 0.7272 | 5.96 | 2100 | 1.0179 | 0.6119 | 0.6143 | 0.6119 | 0.6143 | 0.6143 | 0.6143 | 0.6143 | 0.6147 | 0.6143 | 0.6147 |
| 0.6519 | 6.24 | 2200 | 1.0576 | 0.6246 | 0.6257 | 0.6246 | 0.6257 | 0.6257 | 0.6257 | 0.6257 | 0.6322 | 0.6257 | 0.6322 |
| 0.6287 | 6.52 | 2300 | 1.0537 | 0.6275 | 0.6307 | 0.6275 | 0.6307 | 0.6307 | 0.6307 | 0.6307 | 0.6382 | 0.6307 | 0.6382 |
| 0.6103 | 6.81 | 2400 | 1.0323 | 0.6305 | 0.6328 | 0.6305 | 0.6328 | 0.6328 | 0.6328 | 0.6328 | 0.6329 | 0.6328 | 0.6329 |
| 0.5639 | 7.09 | 2500 | 1.1021 | 0.6306 | 0.6335 | 0.6306 | 0.6335 | 0.6335 | 0.6335 | 0.6335 | 0.6336 | 0.6335 | 0.6336 |
| 0.5706 | 7.38 | 2600 | 1.1086 | 0.6328 | 0.6342 | 0.6328 | 0.6342 | 0.6342 | 0.6342 | 0.6342 | 0.6349 | 0.6342 | 0.6349 |
| 0.529 | 7.66 | 2700 | 1.1428 | 0.6194 | 0.6186 | 0.6194 | 0.6186 | 0.6186 | 0.6186 | 0.6186 | 0.6260 | 0.6186 | 0.6260 |
| 0.5336 | 7.94 | 2800 | 1.1523 | 0.6128 | 0.6136 | 0.6128 | 0.6136 | 0.6136 | 0.6136 | 0.6136 | 0.6131 | 0.6136 | 0.6131 |
| 0.4776 | 8.23 | 2900 | 1.3509 | 0.5922 | 0.5959 | 0.5922 | 0.5959 | 0.5959 | 0.5959 | 0.5959 | 0.6070 | 0.5959 | 0.6070 |
| 0.4603 | 8.51 | 3000 | 1.2143 | 0.6036 | 0.6023 | 0.6036 | 0.6023 | 0.6023 | 0.6023 | 0.6023 | 0.6058 | 0.6023 | 0.6058 |
| 0.4734 | 8.79 | 3100 | 1.2464 | 0.6056 | 0.6051 | 0.6056 | 0.6051 | 0.6051 | 0.6051 | 0.6051 | 0.6063 | 0.6051 | 0.6063 |
| 0.4358 | 9.08 | 3200 | 1.3027 | 0.6110 | 0.6108 | 0.6110 | 0.6108 | 0.6108 | 0.6108 | 0.6108 | 0.6178 | 0.6108 | 0.6178 |
| 0.3808 | 9.36 | 3300 | 1.3469 | 0.6265 | 0.6328 | 0.6265 | 0.6328 | 0.6328 | 0.6328 | 0.6328 | 0.6304 | 0.6328 | 0.6304 |
| 0.4184 | 9.65 | 3400 | 1.3317 | 0.6168 | 0.6165 | 0.6168 | 0.6165 | 0.6165 | 0.6165 | 0.6165 | 0.6220 | 0.6165 | 0.6220 |
| 0.3748 | 9.93 | 3500 | 1.3316 | 0.6232 | 0.625 | 0.6232 | 0.625 | 0.625 | 0.625 | 0.625 | 0.6344 | 0.625 | 0.6344 |
| 0.3785 | 10.21 | 3600 | 1.3792 | 0.6144 | 0.6158 | 0.6144 | 0.6158 | 0.6158 | 0.6158 | 0.6158 | 0.6172 | 0.6158 | 0.6172 |
| 0.3339 | 10.5 | 3700 | 1.4025 | 0.6263 | 0.6264 | 0.6263 | 0.6264 | 0.6264 | 0.6264 | 0.6264 | 0.6296 | 0.6264 | 0.6296 |
| 0.367 | 10.78 | 3800 | 1.3871 | 0.6108 | 0.6115 | 0.6108 | 0.6115 | 0.6115 | 0.6115 | 0.6115 | 0.6135 | 0.6115 | 0.6135 |
| 0.3307 | 11.06 | 3900 | 1.3996 | 0.6170 | 0.6179 | 0.6170 | 0.6179 | 0.6179 | 0.6179 | 0.6179 | 0.6181 | 0.6179 | 0.6181 |
| 0.3188 | 11.35 | 4000 | 1.4383 | 0.6251 | 0.6271 | 0.6251 | 0.6271 | 0.6271 | 0.6271 | 0.6271 | 0.6252 | 0.6271 | 0.6252 |
| 0.3129 | 11.63 | 4100 | 1.4338 | 0.6209 | 0.6214 | 0.6209 | 0.6214 | 0.6214 | 0.6214 | 0.6214 | 0.6217 | 0.6214 | 0.6217 |
| 0.3112 | 11.91 | 4200 | 1.4456 | 0.6275 | 0.6278 | 0.6275 | 0.6278 | 0.6278 | 0.6278 | 0.6278 | 0.6296 | 0.6278 | 0.6296 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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