File size: 1,897 Bytes
3ad8bae 7c5e7c4 3ad8bae 7c5e7c4 8c9aa23 3ad8bae 8c9aa23 3ad8bae 7c5e7c4 8c9aa23 3ad8bae 8c9aa23 3ad8bae 7c5e7c4 8bb70ef 8c9aa23 7c5e7c4 3ad8bae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.58
---
<!-- 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-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7506
- Accuracy: 0.58
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2637 | 0.44 | 25 | 2.1478 | 0.39 |
| 2.0908 | 0.88 | 50 | 1.9407 | 0.51 |
| 1.8916 | 1.32 | 75 | 1.7992 | 0.55 |
| 1.798 | 1.75 | 100 | 1.7506 | 0.58 |
### Framework versions
- Transformers 4.32.0
- Pytorch 1.12.1+cu113
- Datasets 2.14.4
- Tokenizers 0.13.3
|