File size: 2,942 Bytes
d311922 |
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 |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: music-genre-detector-finetuned-gtzan_dset
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: gtzan
metrics:
- name: Accuracy
type: accuracy
value: 0.9298245614035088
- name: Precision
type: precision
value: 0.9292447472185437
- name: Recall
type: recall
value: 0.9298245614035088
- name: F1
type: f1
value: 0.9293437948869628
---
<!-- 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. -->
# music-genre-detector-finetuned-gtzan_dset
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: 0.2288
- Accuracy: 0.9298
- Precision: 0.9292
- Recall: 0.9298
- F1: 0.9293
## 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: 9e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.2522 | 0.98 | 49 | 1.6370 | 0.6090 | 0.6189 | 0.6090 | 0.5764 |
| 1.2901 | 1.98 | 99 | 0.9974 | 0.7556 | 0.7655 | 0.7556 | 0.7426 |
| 1.0046 | 2.99 | 149 | 0.6645 | 0.8195 | 0.8226 | 0.8195 | 0.8162 |
| 0.5952 | 3.99 | 199 | 0.5054 | 0.8459 | 0.8561 | 0.8459 | 0.8460 |
| 0.3596 | 4.99 | 249 | 0.3729 | 0.9023 | 0.9117 | 0.9023 | 0.9041 |
| 0.2534 | 5.99 | 299 | 0.2953 | 0.9073 | 0.9088 | 0.9073 | 0.9075 |
| 0.1413 | 7.0 | 349 | 0.2545 | 0.9223 | 0.9229 | 0.9223 | 0.9216 |
| 0.0759 | 8.0 | 399 | 0.2593 | 0.9198 | 0.9209 | 0.9198 | 0.9190 |
| 0.0491 | 8.98 | 448 | 0.2288 | 0.9298 | 0.9292 | 0.9298 | 0.9293 |
| 0.0355 | 9.82 | 490 | 0.2392 | 0.9223 | 0.9231 | 0.9223 | 0.9221 |
### Framework versions
- Transformers 4.33.1
- Pytorch 1.10.2+cu111
- Datasets 2.14.5
- Tokenizers 0.13.3
|