---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_esp_kin
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# furina_seed42_eng_esp_kin

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: 0.0131
- Spearman Corr: 0.8588

## 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        | 0.6   | 200  | 0.0342          | 0.6070        |
| No log        | 1.21  | 400  | 0.0245          | 0.6934        |
| No log        | 1.81  | 600  | 0.0230          | 0.7199        |
| 0.0458        | 2.42  | 800  | 0.0215          | 0.7448        |
| 0.0458        | 3.02  | 1000 | 0.0203          | 0.7510        |
| 0.0458        | 3.63  | 1200 | 0.0198          | 0.7712        |
| 0.02          | 4.23  | 1400 | 0.0180          | 0.7809        |
| 0.02          | 4.83  | 1600 | 0.0191          | 0.7812        |
| 0.02          | 5.44  | 1800 | 0.0182          | 0.7921        |
| 0.0142        | 6.04  | 2000 | 0.0177          | 0.8010        |
| 0.0142        | 6.65  | 2200 | 0.0170          | 0.8004        |
| 0.0142        | 7.25  | 2400 | 0.0159          | 0.8085        |
| 0.0142        | 7.85  | 2600 | 0.0161          | 0.8114        |
| 0.01          | 8.46  | 2800 | 0.0160          | 0.8142        |
| 0.01          | 9.06  | 3000 | 0.0152          | 0.8218        |
| 0.01          | 9.67  | 3200 | 0.0157          | 0.8234        |
| 0.0072        | 10.27 | 3400 | 0.0145          | 0.8303        |
| 0.0072        | 10.88 | 3600 | 0.0153          | 0.8311        |
| 0.0072        | 11.48 | 3800 | 0.0147          | 0.8311        |
| 0.0059        | 12.08 | 4000 | 0.0140          | 0.8373        |
| 0.0059        | 12.69 | 4200 | 0.0139          | 0.8401        |
| 0.0059        | 13.29 | 4400 | 0.0143          | 0.8406        |
| 0.0059        | 13.9  | 4600 | 0.0136          | 0.8447        |
| 0.0049        | 14.5  | 4800 | 0.0140          | 0.8453        |
| 0.0049        | 15.11 | 5000 | 0.0133          | 0.8452        |
| 0.0049        | 15.71 | 5200 | 0.0140          | 0.8450        |
| 0.0041        | 16.31 | 5400 | 0.0135          | 0.8481        |
| 0.0041        | 16.92 | 5600 | 0.0147          | 0.8489        |
| 0.0041        | 17.52 | 5800 | 0.0135          | 0.8492        |
| 0.0037        | 18.13 | 6000 | 0.0134          | 0.8498        |
| 0.0037        | 18.73 | 6200 | 0.0131          | 0.8492        |
| 0.0037        | 19.34 | 6400 | 0.0134          | 0.8524        |
| 0.0037        | 19.94 | 6600 | 0.0134          | 0.8536        |
| 0.0034        | 20.54 | 6800 | 0.0128          | 0.8540        |
| 0.0034        | 21.15 | 7000 | 0.0134          | 0.8539        |
| 0.0034        | 21.75 | 7200 | 0.0138          | 0.8531        |
| 0.0031        | 22.36 | 7400 | 0.0125          | 0.8562        |
| 0.0031        | 22.96 | 7600 | 0.0135          | 0.8585        |
| 0.0031        | 23.56 | 7800 | 0.0132          | 0.8569        |
| 0.0028        | 24.17 | 8000 | 0.0126          | 0.8564        |
| 0.0028        | 24.77 | 8200 | 0.0130          | 0.8574        |
| 0.0028        | 25.38 | 8400 | 0.0128          | 0.8587        |
| 0.0026        | 25.98 | 8600 | 0.0128          | 0.8595        |
| 0.0026        | 26.59 | 8800 | 0.0131          | 0.8582        |
| 0.0026        | 27.19 | 9000 | 0.0131          | 0.8588        |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1