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---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_amh_hau_cross_latin_2e-05
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_amh_hau_cross_latin_2e-05
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0213
- Spearman Corr: 0.7832
## 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 | 0.52 | 200 | 0.0297 | 0.6717 |
| No log | 1.04 | 400 | 0.0242 | 0.7298 |
| No log | 1.55 | 600 | 0.0238 | 0.7583 |
| 0.0472 | 2.07 | 800 | 0.0234 | 0.7528 |
| 0.0472 | 2.59 | 1000 | 0.0233 | 0.7682 |
| 0.0472 | 3.11 | 1200 | 0.0210 | 0.7792 |
| 0.0472 | 3.63 | 1400 | 0.0206 | 0.7805 |
| 0.0222 | 4.15 | 1600 | 0.0211 | 0.7751 |
| 0.0222 | 4.66 | 1800 | 0.0213 | 0.7796 |
| 0.0222 | 5.18 | 2000 | 0.0207 | 0.7920 |
| 0.0222 | 5.7 | 2200 | 0.0196 | 0.7933 |
| 0.0157 | 6.22 | 2400 | 0.0204 | 0.7839 |
| 0.0157 | 6.74 | 2600 | 0.0204 | 0.7848 |
| 0.0157 | 7.25 | 2800 | 0.0216 | 0.7791 |
| 0.0157 | 7.77 | 3000 | 0.0218 | 0.7844 |
| 0.0116 | 8.29 | 3200 | 0.0214 | 0.7887 |
| 0.0116 | 8.81 | 3400 | 0.0209 | 0.7908 |
| 0.0116 | 9.33 | 3600 | 0.0208 | 0.7768 |
| 0.0116 | 9.84 | 3800 | 0.0213 | 0.7832 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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