---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- mechanicalkeystrokes
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-mechanicalkeystrokes
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: MechanicalKeystrokes
      type: mechanicalkeystrokes
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9666666666666667
---

<!-- 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-mechanicalkeystrokes

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the MechanicalKeystrokes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1155
- Accuracy: 0.9667

## 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: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2923        | 1.0   | 30   | 2.3063          | 0.0333   |
| 2.2788        | 2.0   | 60   | 2.2863          | 0.1333   |
| 2.1377        | 3.0   | 90   | 2.1583          | 0.35     |
| 1.8769        | 4.0   | 120  | 1.8170          | 0.6167   |
| 1.5083        | 5.0   | 150  | 1.4282          | 0.7833   |
| 1.0911        | 6.0   | 180  | 1.0241          | 0.9333   |
| 0.7235        | 7.0   | 210  | 0.7131          | 0.9667   |
| 0.4517        | 8.0   | 240  | 0.4509          | 0.9667   |
| 0.2529        | 9.0   | 270  | 0.2682          | 0.9833   |
| 0.14          | 10.0  | 300  | 0.1799          | 0.9833   |
| 0.0791        | 11.0  | 330  | 0.1155          | 0.9667   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1
- Datasets 2.19.0
- Tokenizers 0.19.1