---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_original_esp-kin-eng_train_spearman_corr
  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_original_esp-kin-eng_train_spearman_corr

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: 0.0243
- Spearman Corr: 0.7225

## 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        | 1.63  | 200  | 0.0285          | 0.5491        |
| 0.0764        | 3.27  | 400  | 0.0332          | 0.6502        |
| 0.0248        | 4.9   | 600  | 0.0243          | 0.6852        |
| 0.0185        | 6.53  | 800  | 0.0217          | 0.7121        |
| 0.0148        | 8.16  | 1000 | 0.0270          | 0.7180        |
| 0.0148        | 9.8   | 1200 | 0.0243          | 0.7170        |
| 0.0121        | 11.43 | 1400 | 0.0240          | 0.7203        |
| 0.01          | 13.06 | 1600 | 0.0250          | 0.7205        |
| 0.0083        | 14.69 | 1800 | 0.0251          | 0.7242        |
| 0.0073        | 16.33 | 2000 | 0.0243          | 0.7153        |
| 0.0073        | 17.96 | 2200 | 0.0257          | 0.7238        |
| 0.0064        | 19.59 | 2400 | 0.0235          | 0.7228        |
| 0.0058        | 21.22 | 2600 | 0.0246          | 0.7193        |
| 0.0054        | 22.86 | 2800 | 0.0246          | 0.7221        |
| 0.0051        | 24.49 | 3000 | 0.0235          | 0.7222        |
| 0.0048        | 26.12 | 3200 | 0.0236          | 0.7224        |
| 0.0048        | 27.76 | 3400 | 0.0243          | 0.7225        |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2