File size: 3,005 Bytes
ff0b9ad bfb8221 ff0b9ad bfb8221 ff0b9ad bfb8221 ff0b9ad 0f8dc28 ff0b9ad 0f8dc28 ff0b9ad bfb8221 ff0b9ad |
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 |
---
library_name: transformers
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: 0.88
---
<!-- 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: 0.5321
- Accuracy: 0.88
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1271 | 1.0 | 113 | 2.0529 | 0.47 |
| 1.4245 | 2.0 | 226 | 1.4173 | 0.6 |
| 1.1783 | 3.0 | 339 | 1.0567 | 0.71 |
| 0.7597 | 4.0 | 452 | 0.8387 | 0.75 |
| 0.6043 | 5.0 | 565 | 0.6876 | 0.81 |
| 0.4758 | 6.0 | 678 | 0.6897 | 0.79 |
| 0.4882 | 7.0 | 791 | 0.6507 | 0.79 |
| 0.2361 | 8.0 | 904 | 0.6232 | 0.84 |
| 0.209 | 9.0 | 1017 | 0.5800 | 0.82 |
| 0.0859 | 10.0 | 1130 | 0.5414 | 0.85 |
| 0.0639 | 11.0 | 1243 | 0.5321 | 0.88 |
| 0.0405 | 12.0 | 1356 | 0.8187 | 0.82 |
| 0.0481 | 13.0 | 1469 | 0.7086 | 0.85 |
| 0.0127 | 14.0 | 1582 | 0.7394 | 0.84 |
| 0.0071 | 15.0 | 1695 | 0.6890 | 0.86 |
| 0.0073 | 16.0 | 1808 | 0.7361 | 0.86 |
| 0.0062 | 17.0 | 1921 | 0.9311 | 0.8 |
| 0.0028 | 18.0 | 2034 | 0.7819 | 0.84 |
| 0.0024 | 19.0 | 2147 | 0.8263 | 0.86 |
| 0.0023 | 20.0 | 2260 | 0.8049 | 0.86 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|