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# distilhubert-finetuned-minds14

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the MINDS14 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4050
- Accuracy: 0.5263

## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.6434        | 1.0   | 253   | 2.6398          | 0.0702   |
| 2.5451        | 2.0   | 506   | 2.6434          | 0.0877   |
| 2.5303        | 3.0   | 759   | 2.6009          | 0.0702   |
| 2.5841        | 4.0   | 1012  | 2.5143          | 0.1053   |
| 2.1201        | 5.0   | 1265  | 2.3397          | 0.1579   |
| 1.6421        | 6.0   | 1518  | 2.1145          | 0.3509   |
| 1.5245        | 7.0   | 1771  | 1.9039          | 0.4386   |
| 1.1713        | 8.0   | 2024  | 1.6985          | 0.3684   |
| 1.1484        | 9.0   | 2277  | 1.5798          | 0.5088   |
| 0.6891        | 10.0  | 2530  | 1.7365          | 0.4912   |
| 0.3124        | 11.0  | 2783  | 1.8768          | 0.4561   |
| 0.077         | 12.0  | 3036  | 2.2095          | 0.4386   |
| 0.0167        | 13.0  | 3289  | 2.3894          | 0.4912   |
| 0.1748        | 14.0  | 3542  | 2.1305          | 0.5789   |
| 0.0366        | 15.0  | 3795  | 2.2102          | 0.5614   |
| 0.0021        | 16.0  | 4048  | 2.2237          | 0.5614   |
| 0.0012        | 17.0  | 4301  | 2.3768          | 0.5263   |
| 0.0009        | 18.0  | 4554  | 2.6185          | 0.4912   |
| 0.0006        | 19.0  | 4807  | 2.5854          | 0.5263   |
| 0.0005        | 20.0  | 5060  | 2.6191          | 0.5965   |
| 0.0004        | 21.0  | 5313  | 2.6767          | 0.5789   |
| 0.0004        | 22.0  | 5566  | 2.7203          | 0.5965   |
| 0.0003        | 23.0  | 5819  | 2.6451          | 0.5965   |
| 0.0003        | 24.0  | 6072  | 2.6883          | 0.5965   |
| 0.0002        | 25.0  | 6325  | 2.7872          | 0.5789   |
| 0.0002        | 26.0  | 6578  | 2.8503          | 0.5789   |
| 0.0002        | 27.0  | 6831  | 2.8895          | 0.5789   |
| 0.0001        | 28.0  | 7084  | 2.8882          | 0.5789   |
| 0.0001        | 29.0  | 7337  | 2.8726          | 0.5439   |
| 0.0001        | 30.0  | 7590  | 2.8971          | 0.5614   |
| 0.0001        | 31.0  | 7843  | 2.9427          | 0.5614   |
| 0.0001        | 32.0  | 8096  | 3.0154          | 0.5439   |
| 0.0001        | 33.0  | 8349  | 3.0109          | 0.5439   |
| 0.0001        | 34.0  | 8602  | 3.0281          | 0.5439   |
| 0.0001        | 35.0  | 8855  | 3.0510          | 0.5439   |
| 0.0001        | 36.0  | 9108  | 3.1110          | 0.5439   |
| 0.0001        | 37.0  | 9361  | 3.1634          | 0.5439   |
| 0.0           | 38.0  | 9614  | 3.1704          | 0.5263   |
| 0.0           | 39.0  | 9867  | 3.2145          | 0.5263   |
| 0.0           | 40.0  | 10120 | 3.2405          | 0.5439   |
| 0.0           | 41.0  | 10373 | 3.2725          | 0.5263   |
| 0.0           | 42.0  | 10626 | 3.2861          | 0.5263   |
| 0.0           | 43.0  | 10879 | 3.3515          | 0.5263   |
| 0.0           | 44.0  | 11132 | 3.3364          | 0.5263   |
| 0.0           | 45.0  | 11385 | 3.3570          | 0.5263   |
| 0.0           | 46.0  | 11638 | 3.3776          | 0.5263   |
| 0.0           | 47.0  | 11891 | 3.3857          | 0.5263   |
| 0.0           | 48.0  | 12144 | 3.3985          | 0.5263   |
| 0.0           | 49.0  | 12397 | 3.4012          | 0.5263   |
| 0.0           | 50.0  | 12650 | 3.4050          | 0.5263   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["PolyAI/minds14"], "metrics": ["accuracy"], "base_model": "ntu-spml/distilhubert", "model-index": [{"name": "distilhubert-finetuned-minds14", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "MINDS14", "type": "PolyAI/minds14", "config": "en-US", "split": "train", "args": "en-US"}, "metrics": [{"type": "accuracy", "value": 0.5263157894736842, "name": "Accuracy"}]}]}]}
transformers tensorboard safetensors hubert audio-classification generated_from_trainer dataset:PolyAI/minds14 base_model:ntu-spml/distilhubert license:apache-2.0 model-index endpoints_compatible region:us