---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- acordes_completo
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-chorddetection
  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. -->

# distilhubert-finetuned-chorddetection

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the ChordStimation dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0           | 1.0   | 3025  | 0.0000          | 1.0      |
| 0.0           | 2.0   | 6050  | 0.0000          | 1.0      |
| 0.0           | 3.0   | 9075  | 0.0000          | 1.0      |
| 0.0           | 4.0   | 12100 | 0.0000          | 1.0      |
| 0.0           | 5.0   | 15125 | 0.0000          | 1.0      |
| 0.0           | 6.0   | 18150 | 0.0000          | 1.0      |
| 0.0           | 7.0   | 21175 | 0.0000          | 1.0      |
| 0.0           | 8.0   | 24200 | 0.0000          | 1.0      |
| 0.0           | 9.0   | 27225 | 0.0000          | 1.0      |
| 0.0           | 10.0  | 30250 | 0.0000          | 1.0      |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1