File size: 4,785 Bytes
d392af9 612ed60 d392af9 612ed60 d392af9 8ff896b 8b45bf4 d392af9 59352e2 612ed60 0723450 d392af9 612ed60 d392af9 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 |
---
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: 0.815
---
<!-- 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: 1.2091
- Accuracy: 0.815
## 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2632 | 1.0 | 67 | 2.2116 | 0.335 |
| 1.8978 | 2.0 | 134 | 1.8129 | 0.5 |
| 1.5811 | 3.0 | 201 | 1.4946 | 0.66 |
| 1.1795 | 4.0 | 268 | 1.2851 | 0.65 |
| 1.0256 | 5.0 | 335 | 1.1538 | 0.66 |
| 0.9168 | 6.0 | 402 | 1.0270 | 0.69 |
| 0.9383 | 7.0 | 469 | 0.9349 | 0.73 |
| 0.5988 | 8.0 | 536 | 0.8443 | 0.795 |
| 0.4844 | 9.0 | 603 | 0.8053 | 0.775 |
| 0.422 | 10.0 | 670 | 0.7710 | 0.785 |
| 0.2138 | 11.0 | 737 | 0.7353 | 0.8 |
| 0.1834 | 12.0 | 804 | 0.8303 | 0.78 |
| 0.1789 | 13.0 | 871 | 0.7801 | 0.805 |
| 0.1649 | 14.0 | 938 | 0.8433 | 0.775 |
| 0.0259 | 15.0 | 1005 | 0.7846 | 0.8 |
| 0.0825 | 16.0 | 1072 | 0.9268 | 0.795 |
| 0.0091 | 17.0 | 1139 | 1.0432 | 0.795 |
| 0.0053 | 18.0 | 1206 | 0.9703 | 0.8 |
| 0.0038 | 19.0 | 1273 | 0.9689 | 0.82 |
| 0.0246 | 20.0 | 1340 | 1.0611 | 0.81 |
| 0.0023 | 21.0 | 1407 | 1.0502 | 0.82 |
| 0.0023 | 22.0 | 1474 | 1.0703 | 0.815 |
| 0.0016 | 23.0 | 1541 | 1.0911 | 0.825 |
| 0.0015 | 24.0 | 1608 | 1.1375 | 0.795 |
| 0.0013 | 25.0 | 1675 | 1.1529 | 0.815 |
| 0.0172 | 26.0 | 1742 | 1.1258 | 0.815 |
| 0.0011 | 27.0 | 1809 | 1.1206 | 0.82 |
| 0.001 | 28.0 | 1876 | 1.1492 | 0.82 |
| 0.0009 | 29.0 | 1943 | 1.1490 | 0.815 |
| 0.0008 | 30.0 | 2010 | 1.1527 | 0.815 |
| 0.0008 | 31.0 | 2077 | 1.2008 | 0.815 |
| 0.0638 | 32.0 | 2144 | 1.1685 | 0.815 |
| 0.0007 | 33.0 | 2211 | 1.1749 | 0.815 |
| 0.0858 | 34.0 | 2278 | 1.1683 | 0.815 |
| 0.0006 | 35.0 | 2345 | 1.1772 | 0.815 |
| 0.0007 | 36.0 | 2412 | 1.1801 | 0.815 |
| 0.0006 | 37.0 | 2479 | 1.1956 | 0.815 |
| 0.0006 | 38.0 | 2546 | 1.1937 | 0.815 |
| 0.0055 | 39.0 | 2613 | 1.2110 | 0.82 |
| 0.0006 | 40.0 | 2680 | 1.2023 | 0.815 |
| 0.0006 | 41.0 | 2747 | 1.2093 | 0.815 |
| 0.001 | 42.0 | 2814 | 1.2075 | 0.815 |
| 0.0006 | 43.0 | 2881 | 1.2079 | 0.815 |
| 0.0662 | 44.0 | 2948 | 1.2054 | 0.815 |
| 0.0006 | 45.0 | 3015 | 1.2066 | 0.815 |
| 0.0006 | 46.0 | 3082 | 1.2089 | 0.815 |
| 0.0006 | 47.0 | 3149 | 1.2093 | 0.815 |
| 0.0005 | 48.0 | 3216 | 1.2096 | 0.815 |
| 0.0005 | 49.0 | 3283 | 1.2094 | 0.815 |
| 0.0006 | 50.0 | 3350 | 1.2091 | 0.815 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|