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TiMauzi
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EraClassifierBiLSTM-4.76M

Audio Classification
PyTorch
Safetensors
music
midi
audio
composer
year
classification
era
song
classical_music
Model card Files
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Community
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1
- ---
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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
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- - f1
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- model-index:
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- - 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: 1.1056
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- - Accuracy: 0.5736
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- - F1: 0.4174
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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
35
-
36
- More information needed
37
-
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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: 4.761974698772928e-05
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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: 5
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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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- | 1.2797 | 0.1031 | 2000 | 1.3522 | 0.4608 | 0.2486 |
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- | 1.1521 | 0.2063 | 4000 | 1.2422 | 0.4987 | 0.3139 |
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- | 1.0887 | 0.3094 | 6000 | 1.2189 | 0.5056 | 0.3223 |
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- | 1.0432 | 0.4126 | 8000 | 1.1715 | 0.5252 | 0.3479 |
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- | 1.019 | 0.5157 | 10000 | 1.2021 | 0.5150 | 0.3304 |
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- | 0.9963 | 0.6188 | 12000 | 1.1789 | 0.5252 | 0.3487 |
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- | 0.976 | 0.7220 | 14000 | 1.1151 | 0.5759 | 0.3983 |
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- | 0.9544 | 0.8251 | 16000 | 1.1800 | 0.5299 | 0.3529 |
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- | 0.9455 | 0.9283 | 18000 | 1.1866 | 0.5415 | 0.3662 |
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- | 0.9276 | 1.0314 | 20000 | 1.1744 | 0.5350 | 0.3792 |
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- | 0.9167 | 1.1345 | 22000 | 1.1032 | 0.5774 | 0.4120 |
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- | 0.9084 | 1.2377 | 24000 | 1.1312 | 0.5553 | 0.3818 |
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- | 0.8758 | 1.3408 | 26000 | 1.1042 | 0.5667 | 0.4109 |
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- | 0.859 | 1.4440 | 28000 | 1.1065 | 0.5733 | 0.4125 |
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- | 0.8607 | 1.5471 | 30000 | 1.1104 | 0.5695 | 0.4115 |
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- | 0.8526 | 1.6503 | 32000 | 1.1011 | 0.5830 | 0.4255 |
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- | 0.8559 | 1.7534 | 34000 | 1.1083 | 0.5765 | 0.4136 |
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- | 0.8501 | 1.8565 | 36000 | 1.1113 | 0.5752 | 0.4163 |
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- | 0.8497 | 1.9597 | 38000 | 1.0935 | 0.5775 | 0.4220 |
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- | 0.8473 | 2.0628 | 40000 | 1.1092 | 0.5745 | 0.4181 |
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- | 0.8441 | 2.1660 | 42000 | 1.1095 | 0.5733 | 0.4164 |
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- | 0.8396 | 2.2691 | 44000 | 1.0935 | 0.5852 | 0.4299 |
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- | 0.8391 | 2.3722 | 46000 | 1.1054 | 0.5744 | 0.4160 |
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- | 0.8401 | 2.4754 | 48000 | 1.1008 | 0.5755 | 0.4198 |
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- | 0.8327 | 2.5785 | 50000 | 1.1097 | 0.5712 | 0.4132 |
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- | 0.838 | 2.6817 | 52000 | 1.1055 | 0.5720 | 0.4143 |
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- | 0.8329 | 2.7848 | 54000 | 1.1055 | 0.5728 | 0.4165 |
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- | 0.8346 | 2.8879 | 56000 | 1.1038 | 0.5743 | 0.4172 |
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- | 0.8353 | 2.9911 | 58000 | 1.1090 | 0.5728 | 0.4167 |
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- | 0.8385 | 3.0942 | 60000 | 1.1013 | 0.5755 | 0.4201 |
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- | 0.8337 | 3.1974 | 62000 | 1.1088 | 0.5733 | 0.4163 |
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- | 0.8256 | 3.3005 | 64000 | 1.1076 | 0.5748 | 0.4177 |
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- | 0.8367 | 3.4036 | 66000 | 1.1066 | 0.5730 | 0.4159 |
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- | 0.831 | 3.5068 | 68000 | 1.1083 | 0.5732 | 0.4164 |
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- | 0.8283 | 3.6099 | 70000 | 1.1067 | 0.5744 | 0.4173 |
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- | 0.8349 | 3.7131 | 72000 | 1.1058 | 0.5747 | 0.4180 |
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- | 0.8313 | 3.8162 | 74000 | 1.1058 | 0.5741 | 0.4171 |
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- | 0.8313 | 3.9193 | 76000 | 1.1065 | 0.5735 | 0.4169 |
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- | 0.8309 | 4.0225 | 78000 | 1.1067 | 0.5736 | 0.4171 |
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- | 0.8331 | 4.1256 | 80000 | 1.1055 | 0.5744 | 0.4174 |
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- | 0.8371 | 4.2288 | 82000 | 1.1058 | 0.5735 | 0.4167 |
97
- | 0.8344 | 4.3319 | 84000 | 1.1060 | 0.5734 | 0.4166 |
98
- | 0.8291 | 4.4350 | 86000 | 1.1049 | 0.5747 | 0.4185 |
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- | 0.8343 | 4.5382 | 88000 | 1.1053 | 0.5735 | 0.4171 |
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- | 0.8293 | 4.6413 | 90000 | 1.1056 | 0.5736 | 0.4174 |
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- | 0.8294 | 4.7445 | 92000 | 1.1056 | 0.5736 | 0.4174 |
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- | 0.8316 | 4.8476 | 94000 | 1.1055 | 0.5736 | 0.4174 |
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- | 0.8264 | 4.9508 | 96000 | 1.1056 | 0.5736 | 0.4174 |
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-
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-
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- ### Framework versions
107
-
108
- - Transformers 4.49.0
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- - Pytorch 2.6.0+cu126
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- - Datasets 3.3.2
111
- - 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: 1.0935
23
+ - Accuracy: 0.5852
24
+ - F1: 0.4299
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: 4.761974698772928e-05
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: 5
50
+ - mixed_precision_training: Native AMP
51
+
52
+ ### Training results
53
+
54
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
55
+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
56
+ | 1.2797 | 0.1031 | 2000 | 1.3522 | 0.4608 | 0.2486 |
57
+ | 1.1521 | 0.2063 | 4000 | 1.2422 | 0.4987 | 0.3139 |
58
+ | 1.0887 | 0.3094 | 6000 | 1.2189 | 0.5056 | 0.3223 |
59
+ | 1.0432 | 0.4126 | 8000 | 1.1715 | 0.5252 | 0.3479 |
60
+ | 1.019 | 0.5157 | 10000 | 1.2021 | 0.5150 | 0.3304 |
61
+ | 0.9963 | 0.6188 | 12000 | 1.1789 | 0.5252 | 0.3487 |
62
+ | 0.976 | 0.7220 | 14000 | 1.1151 | 0.5759 | 0.3983 |
63
+ | 0.9544 | 0.8251 | 16000 | 1.1800 | 0.5299 | 0.3529 |
64
+ | 0.9455 | 0.9283 | 18000 | 1.1866 | 0.5415 | 0.3662 |
65
+ | 0.9276 | 1.0314 | 20000 | 1.1744 | 0.5350 | 0.3792 |
66
+ | 0.9167 | 1.1345 | 22000 | 1.1032 | 0.5774 | 0.4120 |
67
+ | 0.9084 | 1.2377 | 24000 | 1.1312 | 0.5553 | 0.3818 |
68
+ | 0.8758 | 1.3408 | 26000 | 1.1042 | 0.5667 | 0.4109 |
69
+ | 0.859 | 1.4440 | 28000 | 1.1065 | 0.5733 | 0.4125 |
70
+ | 0.8607 | 1.5471 | 30000 | 1.1104 | 0.5695 | 0.4115 |
71
+ | 0.8526 | 1.6503 | 32000 | 1.1011 | 0.5830 | 0.4255 |
72
+ | 0.8559 | 1.7534 | 34000 | 1.1083 | 0.5765 | 0.4136 |
73
+ | 0.8501 | 1.8565 | 36000 | 1.1113 | 0.5752 | 0.4163 |
74
+ | 0.8497 | 1.9597 | 38000 | 1.0935 | 0.5775 | 0.4220 |
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+ | 0.8473 | 2.0628 | 40000 | 1.1092 | 0.5745 | 0.4181 |
76
+ | 0.8441 | 2.1660 | 42000 | 1.1095 | 0.5733 | 0.4164 |
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+ | 0.8396 | 2.2691 | 44000 | 1.0935 | 0.5852 | 0.4299 |
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+ | 0.8391 | 2.3722 | 46000 | 1.1054 | 0.5744 | 0.4160 |
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+ | 0.8401 | 2.4754 | 48000 | 1.1008 | 0.5755 | 0.4198 |
80
+ | 0.8327 | 2.5785 | 50000 | 1.1097 | 0.5712 | 0.4132 |
81
+ | 0.838 | 2.6817 | 52000 | 1.1055 | 0.5720 | 0.4143 |
82
+ | 0.8329 | 2.7848 | 54000 | 1.1055 | 0.5728 | 0.4165 |
83
+ | 0.8346 | 2.8879 | 56000 | 1.1038 | 0.5743 | 0.4172 |
84
+ | 0.8353 | 2.9911 | 58000 | 1.1090 | 0.5728 | 0.4167 |
85
+ | 0.8385 | 3.0942 | 60000 | 1.1013 | 0.5755 | 0.4201 |
86
+ | 0.8337 | 3.1974 | 62000 | 1.1088 | 0.5733 | 0.4163 |
87
+ | 0.8256 | 3.3005 | 64000 | 1.1076 | 0.5748 | 0.4177 |
88
+ | 0.8367 | 3.4036 | 66000 | 1.1066 | 0.5730 | 0.4159 |
89
+ | 0.831 | 3.5068 | 68000 | 1.1083 | 0.5732 | 0.4164 |
90
+ | 0.8283 | 3.6099 | 70000 | 1.1067 | 0.5744 | 0.4173 |
91
+ | 0.8349 | 3.7131 | 72000 | 1.1058 | 0.5747 | 0.4180 |
92
+ | 0.8313 | 3.8162 | 74000 | 1.1058 | 0.5741 | 0.4171 |
93
+ | 0.8313 | 3.9193 | 76000 | 1.1065 | 0.5735 | 0.4169 |
94
+ | 0.8309 | 4.0225 | 78000 | 1.1067 | 0.5736 | 0.4171 |
95
+ | 0.8331 | 4.1256 | 80000 | 1.1055 | 0.5744 | 0.4174 |
96
+ | 0.8371 | 4.2288 | 82000 | 1.1058 | 0.5735 | 0.4167 |
97
+ | 0.8344 | 4.3319 | 84000 | 1.1060 | 0.5734 | 0.4166 |
98
+ | 0.8291 | 4.4350 | 86000 | 1.1049 | 0.5747 | 0.4185 |
99
+ | 0.8343 | 4.5382 | 88000 | 1.1053 | 0.5735 | 0.4171 |
100
+ | 0.8293 | 4.6413 | 90000 | 1.1056 | 0.5736 | 0.4174 |
101
+ | 0.8294 | 4.7445 | 92000 | 1.1056 | 0.5736 | 0.4174 |
102
+ | 0.8316 | 4.8476 | 94000 | 1.1055 | 0.5736 | 0.4174 |
103
+ | 0.8264 | 4.9508 | 96000 | 1.1056 | 0.5736 | 0.4174 |
104
+
105
+
106
+ ### Framework versions
107
+
108
+ - Transformers 4.49.0
109
+ - Pytorch 2.6.0+cu126
110
+ - Datasets 3.3.2
111
+ - Tokenizers 0.21.0