---
tags:
- generated_from_trainer
model-index:
- name: chord_model
  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. -->

# chord_model

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4598

## 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: 444
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.3
- training_steps: 0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2264        | 0.11  | 500   | 1.1269          |
| 0.9624        | 0.21  | 1000  | 0.9066          |
| 0.8598        | 0.32  | 1500  | 0.8128          |
| 0.8209        | 0.43  | 2000  | 0.7626          |
| 0.7483        | 0.53  | 2500  | 0.7272          |
| 0.7391        | 0.64  | 3000  | 0.7032          |
| 0.7052        | 0.75  | 3500  | 0.6739          |
| 0.6998        | 0.86  | 4000  | 0.6503          |
| 0.6901        | 0.96  | 4500  | 0.6244          |
| 0.6348        | 1.07  | 5000  | 0.6100          |
| 0.654         | 1.18  | 5500  | 0.5891          |
| 0.6227        | 1.28  | 6000  | 0.5765          |
| 0.6148        | 1.39  | 6500  | 0.5624          |
| 0.5973        | 1.5   | 7000  | 0.5538          |
| 0.5853        | 1.6   | 7500  | 0.5441          |
| 0.56          | 1.71  | 8000  | 0.5407          |
| 0.574         | 1.82  | 8500  | 0.5342          |
| 0.5589        | 1.92  | 9000  | 0.5296          |
| 0.5634        | 2.03  | 9500  | 0.5254          |
| 0.543         | 2.14  | 10000 | 0.5208          |
| 0.5792        | 2.25  | 10500 | 0.5159          |
| 0.5571        | 2.35  | 11000 | 0.5064          |
| 0.5408        | 2.46  | 11500 | 0.4957          |
| 0.5398        | 2.57  | 12000 | 0.4882          |
| 0.537         | 2.67  | 12500 | 0.4834          |
| 0.5512        | 2.78  | 13000 | 0.4786          |
| 0.4842        | 2.89  | 13500 | 0.4753          |
| 0.5275        | 2.99  | 14000 | 0.4721          |
| 0.4899        | 3.1   | 14500 | 0.4710          |
| 0.5222        | 3.21  | 15000 | 0.4666          |
| 0.4929        | 3.31  | 15500 | 0.4645          |
| 0.5049        | 3.42  | 16000 | 0.4631          |
| 0.5002        | 3.53  | 16500 | 0.4613          |
| 0.505         | 3.64  | 17000 | 0.4611          |
| 0.507         | 3.74  | 17500 | 0.4602          |
| 0.5169        | 3.85  | 18000 | 0.4598          |
| 0.501         | 3.96  | 18500 | 0.4598          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1