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---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-minds14
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: MINDS14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.5263157894736842
---
<!-- 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-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
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