File size: 3,197 Bytes
4c1ef54 22dfe7b 4c1ef54 22dfe7b 4c1ef54 22dfe7b 4c1ef54 11fd234 4c1ef54 0c551ce 11fd234 4c1ef54 11fd234 4c1ef54 0c551ce 4c1ef54 0c551ce 4c1ef54 22dfe7b 4c1ef54 22dfe7b |
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 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan-v2
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan-v2
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.82
---
<!-- 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: 0.7819
- Accuracy: 0.82
## 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: 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: 0.3
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.286 | 1.0 | 113 | 2.2792 | 0.26 |
| 2.1863 | 2.0 | 226 | 2.1408 | 0.34 |
| 1.9386 | 3.0 | 339 | 1.8744 | 0.48 |
| 1.6908 | 4.0 | 452 | 1.6502 | 0.57 |
| 1.5259 | 5.0 | 565 | 1.4149 | 0.72 |
| 1.1279 | 6.0 | 678 | 1.2700 | 0.62 |
| 1.2204 | 7.0 | 791 | 0.9902 | 0.75 |
| 0.861 | 8.0 | 904 | 0.8020 | 0.8 |
| 0.8153 | 9.0 | 1017 | 0.7291 | 0.8 |
| 0.3983 | 10.0 | 1130 | 0.7304 | 0.8 |
| 0.2209 | 11.0 | 1243 | 0.6960 | 0.79 |
| 0.2523 | 12.0 | 1356 | 0.5783 | 0.83 |
| 0.1267 | 13.0 | 1469 | 0.5613 | 0.83 |
| 0.0468 | 14.0 | 1582 | 0.7976 | 0.8 |
| 0.025 | 15.0 | 1695 | 0.8478 | 0.81 |
| 0.0158 | 16.0 | 1808 | 0.7448 | 0.8 |
| 0.0706 | 17.0 | 1921 | 0.7183 | 0.83 |
| 0.0096 | 18.0 | 2034 | 0.7532 | 0.82 |
| 0.0076 | 19.0 | 2147 | 0.7907 | 0.81 |
| 0.0354 | 20.0 | 2260 | 0.7120 | 0.83 |
| 0.0063 | 21.0 | 2373 | 0.7525 | 0.83 |
| 0.0055 | 22.0 | 2486 | 0.7647 | 0.82 |
| 0.0049 | 23.0 | 2599 | 0.7945 | 0.82 |
| 0.0048 | 24.0 | 2712 | 0.7982 | 0.82 |
| 0.0321 | 25.0 | 2825 | 0.7819 | 0.82 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|