README.md

metadata

license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: distilhubert-finetuned-gtzan-4
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: GTZAN
          type: marsyas/gtzan
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.82

distilhubert-finetuned-gtzan-4

This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:

• Loss: 2.8452
• 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: 5e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 80

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2676	1.0	225	2.2400	0.33
1.9086	2.0	450	1.8716	0.55
1.417	3.0	675	1.4094	0.69
1.0484	4.0	900	1.1065	0.77
1.0122	5.0	1125	0.9172	0.75
0.8937	6.0	1350	1.1343	0.66
0.2979	7.0	1575	0.6102	0.83
0.3413	8.0	1800	1.1212	0.71
1.459	9.0	2025	1.5433	0.75
0.5177	10.0	2250	1.3990	0.78
0.0494	11.0	2475	2.7712	0.78
0.792	12.0	2700	2.7047	0.81
0.0	13.0	2925	2.8097	0.82
0.0	14.0	3150	3.3873	0.79
0.0	15.0	3375	2.6185	0.81
0.0	16.0	3600	3.0773	0.81
0.0	17.0	3825	2.2380	0.84
0.0	18.0	4050	2.5949	0.79
0.0	19.0	4275	3.4890	0.75
0.0	20.0	4500	2.7776	0.82
0.0	21.0	4725	3.8952	0.77
0.0	22.0	4950	2.8020	0.8
0.0	23.0	5175	3.7968	0.72
0.0	24.0	5400	2.8630	0.81
0.0	25.0	5625	2.2026	0.84
0.0	26.0	5850	1.8612	0.87
0.0	27.0	6075	3.2650	0.78
0.0	28.0	6300	2.6739	0.82
0.0	29.0	6525	3.0167	0.8
0.0	30.0	6750	1.9316	0.84
0.0	31.0	6975	3.0458	0.8
0.0	32.0	7200	3.3569	0.77
0.0	33.0	7425	3.0012	0.79
0.0	34.0	7650	3.2477	0.79
0.0	35.0	7875	3.2145	0.79
0.0	36.0	8100	3.0645	0.79
0.0	37.0	8325	3.2974	0.77
0.0	38.0	8550	3.4422	0.77
0.0	39.0	8775	2.7268	0.8
0.0	40.0	9000	2.6908	0.8
0.0	41.0	9225	2.4034	0.82
0.0	42.0	9450	3.1446	0.79
0.0	43.0	9675	2.9127	0.8
0.0	44.0	9900	2.3812	0.81
0.0	45.0	10125	2.4215	0.81
0.0	46.0	10350	2.6125	0.82
0.7338	47.0	10575	2.5113	0.82
0.0	48.0	10800	2.9264	0.81
0.0	49.0	11025	2.7811	0.81
0.0	50.0	11250	2.6749	0.8
0.0	51.0	11475	3.2003	0.78
0.0	52.0	11700	3.2670	0.78
0.0	53.0	11925	3.4001	0.76
0.0	54.0	12150	2.8570	0.76
0.0	55.0	12375	2.1772	0.83
0.0	56.0	12600	2.7977	0.81
0.0	57.0	12825	2.9106	0.78
0.0	58.0	13050	2.8428	0.8
0.0	59.0	13275	2.7308	0.78
0.0	60.0	13500	2.8214	0.8
0.0	61.0	13725	2.8194	0.79
0.0	62.0	13950	1.7708	0.85
0.0	63.0	14175	2.6017	0.81
0.0	64.0	14400	2.7698	0.78
0.0	65.0	14625	2.8218	0.81
0.0	66.0	14850	2.8252	0.82
0.0	67.0	15075	2.9149	0.81
0.0	68.0	15300	2.8106	0.81
0.0	69.0	15525	2.8514	0.8
0.0	70.0	15750	2.6649	0.82
0.0	71.0	15975	2.5629	0.81
0.0	72.0	16200	2.8140	0.81
0.0	73.0	16425	2.8164	0.79
0.0	74.0	16650	2.7022	0.81
0.0	75.0	16875	2.7376	0.81
0.0	76.0	17100	2.6498	0.8
0.0	77.0	17325	2.7363	0.81
0.0	78.0	17550	2.8057	0.81
0.0	79.0	17775	2.8526	0.81
0.0	80.0	18000	2.8452	0.82

Framework versions

• Transformers 4.33.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.14.4
• Tokenizers 0.13.3