transformers tensorboard safetensors hubert audio-classification generated_from_trainer dataset:audiofolder base_model:anton-l/distilhubert-ft-keyword-spotting license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: anton-l/distilhubert-ft-keyword-spotting
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: distilhubert-ft-keyword-spotting-finetuned-ks-ob
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9850014526438118
---

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# distilhubert-ft-keyword-spotting-finetuned-ks-ob

This model is a fine-tuned version of [anton-l/distilhubert-ft-keyword-spotting](https://huggingface.co/anton-l/distilhubert-ft-keyword-spotting) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0459
- Accuracy: 0.9850

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1536        | 1.0   | 215  | 0.1282          | 0.9606   |
| 0.0809        | 2.0   | 430  | 0.0752          | 0.9763   |
| 0.0839        | 3.0   | 645  | 0.0638          | 0.9783   |
| 0.0536        | 4.0   | 861  | 0.0588          | 0.9794   |
| 0.0412        | 4.99  | 1075 | 0.0459          | 0.9850   |


### Framework versions

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