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EraClassifierBiLSTM-134M

Audio Classification
PyTorch
Safetensors
music
midi
audio
composer
year
classification
era
song
classical_music
Model card Files
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- ---
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- library_name: transformers
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- tags:
4
- - generated_from_trainer
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- datasets:
6
- - generator
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- metrics:
8
- - accuracy
9
- - f1
10
- model-index:
11
- - name: EraClassifierBiLSTM
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- results: []
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- ---
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-
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- <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- should probably proofread and complete it, then remove this comment. -->
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-
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- # EraClassifierBiLSTM
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-
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- This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 1.0269
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- - Accuracy: 0.6593
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- - F1: 0.5103
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-
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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-
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- ## Training procedure
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-
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- ### Training hyperparameters
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-
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- The following hyperparameters were used during training:
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- - learning_rate: 0.0006535848403050624
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- - train_batch_size: 64
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- - eval_batch_size: 64
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- - seed: 42
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- - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- - lr_scheduler_type: reduce_lr_on_plateau
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- - num_epochs: 3
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- - mixed_precision_training: Native AMP
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-
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- ### Training results
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-
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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- |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
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- | 1.1478 | 0.1031 | 2000 | 1.1945 | 0.5275 | 0.3487 |
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- | 0.9699 | 0.2063 | 4000 | 1.0621 | 0.6357 | 0.4551 |
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- | 0.9049 | 0.3094 | 6000 | 1.0657 | 0.5898 | 0.4074 |
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- | 0.8577 | 0.4126 | 8000 | 1.0708 | 0.6032 | 0.4562 |
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- | 0.8293 | 0.5157 | 10000 | 1.0425 | 0.6096 | 0.4274 |
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- | 0.8002 | 0.6188 | 12000 | 1.0197 | 0.6157 | 0.4464 |
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- | 0.7799 | 0.7220 | 14000 | 1.0540 | 0.6103 | 0.4576 |
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- | 0.7545 | 0.8251 | 16000 | 1.0288 | 0.6266 | 0.4682 |
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- | 0.7415 | 0.9283 | 18000 | 1.0332 | 0.6206 | 0.4614 |
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- | 0.7205 | 1.0314 | 20000 | 1.0262 | 0.6333 | 0.4734 |
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- | 0.7005 | 1.1345 | 22000 | 0.9989 | 0.6363 | 0.4840 |
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- | 0.6924 | 1.2377 | 24000 | 1.0136 | 0.6347 | 0.4647 |
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- | 0.6541 | 1.3408 | 26000 | 0.9917 | 0.6466 | 0.4951 |
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- | 0.6261 | 1.4440 | 28000 | 0.9876 | 0.6465 | 0.4924 |
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- | 0.6271 | 1.5471 | 30000 | 1.0057 | 0.6449 | 0.4976 |
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- | 0.6124 | 1.6503 | 32000 | 0.9994 | 0.6494 | 0.5007 |
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- | 0.6137 | 1.7534 | 34000 | 1.0015 | 0.6493 | 0.4976 |
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- | 0.604 | 1.8565 | 36000 | 1.0058 | 0.6524 | 0.4997 |
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- | 0.6063 | 1.9597 | 38000 | 1.0046 | 0.6512 | 0.5032 |
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- | 0.5859 | 2.0628 | 40000 | 1.0162 | 0.6572 | 0.5121 |
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- | 0.5778 | 2.1660 | 42000 | 1.0052 | 0.6591 | 0.5089 |
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- | 0.5679 | 2.2691 | 44000 | 1.0288 | 0.6539 | 0.5044 |
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- | 0.5646 | 2.3722 | 46000 | 1.0247 | 0.6559 | 0.5085 |
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- | 0.5693 | 2.4754 | 48000 | 1.0250 | 0.6581 | 0.5096 |
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- | 0.5607 | 2.5785 | 50000 | 1.0296 | 0.6573 | 0.5069 |
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- | 0.5641 | 2.6817 | 52000 | 1.0266 | 0.6573 | 0.5080 |
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- | 0.5601 | 2.7848 | 54000 | 1.0268 | 0.6577 | 0.5098 |
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- | 0.5607 | 2.8879 | 56000 | 1.0263 | 0.6539 | 0.5060 |
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- | 0.5582 | 2.9911 | 58000 | 1.0269 | 0.6593 | 0.5103 |
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-
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-
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- ### Framework versions
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-
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- - Transformers 4.49.0
90
- - Pytorch 2.6.0+cu126
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- - Datasets 3.3.2
92
- - Tokenizers 0.21.0
 
1
+ ---
2
+ library_name: transformers
3
+ tags:
4
+ - generated_from_trainer
5
+ datasets:
6
+ - generator
7
+ metrics:
8
+ - accuracy
9
+ - f1
10
+ model-index:
11
+ - name: EraClassifierBiLSTM
12
+ results: []
13
+ ---
14
+
15
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
16
+ should probably proofread and complete it, then remove this comment. -->
17
+
18
+ # EraClassifierBiLSTM
19
+
20
+ This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset.
21
+ It achieves the following results on the evaluation set:
22
+ - Loss: 1.0162
23
+ - Accuracy: 0.6572
24
+ - F1: 0.5121
25
+
26
+ ## Model description
27
+
28
+ More information needed
29
+
30
+ ## Intended uses & limitations
31
+
32
+ More information needed
33
+
34
+ ## Training and evaluation data
35
+
36
+ More information needed
37
+
38
+ ## Training procedure
39
+
40
+ ### Training hyperparameters
41
+
42
+ The following hyperparameters were used during training:
43
+ - learning_rate: 0.0006535848403050624
44
+ - train_batch_size: 64
45
+ - eval_batch_size: 64
46
+ - seed: 42
47
+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
48
+ - lr_scheduler_type: reduce_lr_on_plateau
49
+ - num_epochs: 3
50
+ - mixed_precision_training: Native AMP
51
+
52
+ ### Training results
53
+
54
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
55
+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
56
+ | 1.1478 | 0.1031 | 2000 | 1.1945 | 0.5275 | 0.3487 |
57
+ | 0.9699 | 0.2063 | 4000 | 1.0621 | 0.6357 | 0.4551 |
58
+ | 0.9049 | 0.3094 | 6000 | 1.0657 | 0.5898 | 0.4074 |
59
+ | 0.8577 | 0.4126 | 8000 | 1.0708 | 0.6032 | 0.4562 |
60
+ | 0.8293 | 0.5157 | 10000 | 1.0425 | 0.6096 | 0.4274 |
61
+ | 0.8002 | 0.6188 | 12000 | 1.0197 | 0.6157 | 0.4464 |
62
+ | 0.7799 | 0.7220 | 14000 | 1.0540 | 0.6103 | 0.4576 |
63
+ | 0.7545 | 0.8251 | 16000 | 1.0288 | 0.6266 | 0.4682 |
64
+ | 0.7415 | 0.9283 | 18000 | 1.0332 | 0.6206 | 0.4614 |
65
+ | 0.7205 | 1.0314 | 20000 | 1.0262 | 0.6333 | 0.4734 |
66
+ | 0.7005 | 1.1345 | 22000 | 0.9989 | 0.6363 | 0.4840 |
67
+ | 0.6924 | 1.2377 | 24000 | 1.0136 | 0.6347 | 0.4647 |
68
+ | 0.6541 | 1.3408 | 26000 | 0.9917 | 0.6466 | 0.4951 |
69
+ | 0.6261 | 1.4440 | 28000 | 0.9876 | 0.6465 | 0.4924 |
70
+ | 0.6271 | 1.5471 | 30000 | 1.0057 | 0.6449 | 0.4976 |
71
+ | 0.6124 | 1.6503 | 32000 | 0.9994 | 0.6494 | 0.5007 |
72
+ | 0.6137 | 1.7534 | 34000 | 1.0015 | 0.6493 | 0.4976 |
73
+ | 0.604 | 1.8565 | 36000 | 1.0058 | 0.6524 | 0.4997 |
74
+ | 0.6063 | 1.9597 | 38000 | 1.0046 | 0.6512 | 0.5032 |
75
+ | 0.5859 | 2.0628 | 40000 | 1.0162 | 0.6572 | 0.5121 |
76
+ | 0.5778 | 2.1660 | 42000 | 1.0052 | 0.6591 | 0.5089 |
77
+ | 0.5679 | 2.2691 | 44000 | 1.0288 | 0.6539 | 0.5044 |
78
+ | 0.5646 | 2.3722 | 46000 | 1.0247 | 0.6559 | 0.5085 |
79
+ | 0.5693 | 2.4754 | 48000 | 1.0250 | 0.6581 | 0.5096 |
80
+ | 0.5607 | 2.5785 | 50000 | 1.0296 | 0.6573 | 0.5069 |
81
+ | 0.5641 | 2.6817 | 52000 | 1.0266 | 0.6573 | 0.5080 |
82
+ | 0.5601 | 2.7848 | 54000 | 1.0268 | 0.6577 | 0.5098 |
83
+ | 0.5607 | 2.8879 | 56000 | 1.0263 | 0.6539 | 0.5060 |
84
+ | 0.5582 | 2.9911 | 58000 | 1.0269 | 0.6593 | 0.5103 |
85
+
86
+
87
+ ### Framework versions
88
+
89
+ - Transformers 4.49.0
90
+ - Pytorch 2.6.0+cu126
91
+ - Datasets 3.3.2
92
+ - Tokenizers 0.21.0