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---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_kin_hau
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. -->
# furina_seed42_eng_kin_hau
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0202
- Spearman Corr: 0.7925
## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 0.59 | 200 | 0.0427 | 0.4859 |
| No log | 1.18 | 400 | 0.0345 | 0.6546 |
| No log | 1.77 | 600 | 0.0295 | 0.6863 |
| 0.0527 | 2.36 | 800 | 0.0272 | 0.6929 |
| 0.0527 | 2.95 | 1000 | 0.0284 | 0.7195 |
| 0.0527 | 3.55 | 1200 | 0.0271 | 0.7192 |
| 0.0244 | 4.14 | 1400 | 0.0253 | 0.7366 |
| 0.0244 | 4.73 | 1600 | 0.0236 | 0.7434 |
| 0.0244 | 5.32 | 1800 | 0.0239 | 0.7436 |
| 0.0244 | 5.91 | 2000 | 0.0255 | 0.7483 |
| 0.017 | 6.5 | 2200 | 0.0230 | 0.7575 |
| 0.017 | 7.09 | 2400 | 0.0231 | 0.7541 |
| 0.017 | 7.68 | 2600 | 0.0230 | 0.7568 |
| 0.0123 | 8.27 | 2800 | 0.0228 | 0.7575 |
| 0.0123 | 8.86 | 3000 | 0.0233 | 0.7633 |
| 0.0123 | 9.45 | 3200 | 0.0228 | 0.7679 |
| 0.0092 | 10.04 | 3400 | 0.0226 | 0.7647 |
| 0.0092 | 10.64 | 3600 | 0.0220 | 0.7704 |
| 0.0092 | 11.23 | 3800 | 0.0214 | 0.7717 |
| 0.0092 | 11.82 | 4000 | 0.0219 | 0.7768 |
| 0.0074 | 12.41 | 4200 | 0.0215 | 0.7760 |
| 0.0074 | 13.0 | 4400 | 0.0209 | 0.7792 |
| 0.0074 | 13.59 | 4600 | 0.0206 | 0.7796 |
| 0.006 | 14.18 | 4800 | 0.0211 | 0.7770 |
| 0.006 | 14.77 | 5000 | 0.0211 | 0.7801 |
| 0.006 | 15.36 | 5200 | 0.0216 | 0.7807 |
| 0.006 | 15.95 | 5400 | 0.0205 | 0.7841 |
| 0.0052 | 16.54 | 5600 | 0.0211 | 0.7846 |
| 0.0052 | 17.13 | 5800 | 0.0206 | 0.7873 |
| 0.0052 | 17.73 | 6000 | 0.0202 | 0.7863 |
| 0.0045 | 18.32 | 6200 | 0.0205 | 0.7858 |
| 0.0045 | 18.91 | 6400 | 0.0202 | 0.7886 |
| 0.0045 | 19.5 | 6600 | 0.0208 | 0.7876 |
| 0.0042 | 20.09 | 6800 | 0.0203 | 0.7902 |
| 0.0042 | 20.68 | 7000 | 0.0211 | 0.7850 |
| 0.0042 | 21.27 | 7200 | 0.0204 | 0.7899 |
| 0.0042 | 21.86 | 7400 | 0.0207 | 0.7905 |
| 0.0037 | 22.45 | 7600 | 0.0202 | 0.7925 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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