Hugging Face's logo

callmesan
/
vakyansh-wav2vec2-punjabi-pam-10-audio-abuse-feature

Audio Classification
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Community
callmesan commited on
Commit
f714f65
•
1 Parent(s): 1189031

End of training

Browse files
Files changed (1) hide show
  1. README.md +128 -0
README.md ADDED
@@ -0,0 +1,128 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ base_model: Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10
4
+ tags:
5
+ - generated_from_trainer
6
+ metrics:
7
+ - accuracy
8
+ model-index:
9
+ - name: vakyansh-wav2vec2-punjabi-pam-10-audio-abuse-feature
10
+ results: []
11
+ ---
12
+
13
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
+ should probably proofread and complete it, then remove this comment. -->
15
+
16
+ # vakyansh-wav2vec2-punjabi-pam-10-audio-abuse-feature
17
+
18
+ This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10) on the None dataset.
19
+ It achieves the following results on the evaluation set:
20
+ - Loss: 0.7070
21
+ - Accuracy: 0.7112
22
+ - Macro F1-score: 0.7112
23
+
24
+ ## Model description
25
+
26
+ More information needed
27
+
28
+ ## Intended uses & limitations
29
+
30
+ More information needed
31
+
32
+ ## Training and evaluation data
33
+
34
+ More information needed
35
+
36
+ ## Training procedure
37
+
38
+ ### Training hyperparameters
39
+
40
+ The following hyperparameters were used during training:
41
+ - learning_rate: 2e-05
42
+ - train_batch_size: 16
43
+ - eval_batch_size: 16
44
+ - seed: 42
45
+ - gradient_accumulation_steps: 4
46
+ - total_train_batch_size: 64
47
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
48
+ - lr_scheduler_type: linear
49
+ - lr_scheduler_warmup_ratio: 0.1
50
+ - num_epochs: 50
51
+
52
+ ### Training results
53
+
54
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1-score |
55
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------:|
56
+ | 6.7326 | 0.77 | 10 | 6.7224 | 0.0 | 0.0 |
57
+ | 6.682 | 1.54 | 20 | 6.5714 | 0.2888 | 0.0298 |
58
+ | 6.523 | 2.31 | 30 | 6.3268 | 0.4877 | 0.3619 |
59
+ | 6.247 | 3.08 | 40 | 6.0039 | 0.4768 | 0.3229 |
60
+ | 6.0644 | 3.85 | 50 | 5.7134 | 0.4796 | 0.3241 |
61
+ | 5.7866 | 4.62 | 60 | 5.4332 | 0.4796 | 0.3241 |
62
+ | 5.5229 | 5.38 | 70 | 5.2026 | 0.4796 | 0.3241 |
63
+ | 5.2712 | 6.15 | 80 | 4.9856 | 0.4796 | 0.3241 |
64
+ | 5.1268 | 6.92 | 90 | 4.7918 | 0.4796 | 0.3241 |
65
+ | 4.9768 | 7.69 | 100 | 4.5999 | 0.4796 | 0.3241 |
66
+ | 4.7137 | 8.46 | 110 | 4.3958 | 0.4796 | 0.3241 |
67
+ | 4.5863 | 9.23 | 120 | 4.1988 | 0.4796 | 0.3241 |
68
+ | 4.3386 | 10.0 | 130 | 3.9983 | 0.4796 | 0.3241 |
69
+ | 4.1936 | 10.77 | 140 | 3.7938 | 0.4796 | 0.3241 |
70
+ | 3.9752 | 11.54 | 150 | 3.5906 | 0.4796 | 0.3241 |
71
+ | 3.9035 | 12.31 | 160 | 3.3854 | 0.4796 | 0.3241 |
72
+ | 3.652 | 13.08 | 170 | 3.1907 | 0.4796 | 0.3241 |
73
+ | 3.3045 | 13.85 | 180 | 2.9781 | 0.4796 | 0.3241 |
74
+ | 3.135 | 14.62 | 190 | 2.7764 | 0.4796 | 0.3241 |
75
+ | 2.9589 | 15.38 | 200 | 2.5827 | 0.4796 | 0.3241 |
76
+ | 2.7405 | 16.15 | 210 | 2.3901 | 0.4796 | 0.3241 |
77
+ | 2.5482 | 16.92 | 220 | 2.2042 | 0.4796 | 0.3241 |
78
+ | 2.4126 | 17.69 | 230 | 2.0318 | 0.4796 | 0.3241 |
79
+ | 2.2721 | 18.46 | 240 | 1.8672 | 0.4796 | 0.3241 |
80
+ | 2.0507 | 19.23 | 250 | 1.7156 | 0.4796 | 0.3241 |
81
+ | 1.8895 | 20.0 | 260 | 1.5721 | 0.4796 | 0.3241 |
82
+ | 1.7304 | 20.77 | 270 | 1.4453 | 0.4796 | 0.3241 |
83
+ | 1.5756 | 21.54 | 280 | 1.3330 | 0.4796 | 0.3241 |
84
+ | 1.4961 | 22.31 | 290 | 1.2238 | 0.6594 | 0.6321 |
85
+ | 1.4065 | 23.08 | 300 | 1.1468 | 0.6621 | 0.6356 |
86
+ | 1.4168 | 23.85 | 310 | 1.0636 | 0.6839 | 0.6632 |
87
+ | 1.1788 | 24.62 | 320 | 0.9818 | 0.7411 | 0.7325 |
88
+ | 1.06 | 25.38 | 330 | 0.9203 | 0.7466 | 0.7438 |
89
+ | 1.0021 | 26.15 | 340 | 0.8806 | 0.7629 | 0.7629 |
90
+ | 1.0249 | 26.92 | 350 | 0.8698 | 0.6894 | 0.6690 |
91
+ | 0.8521 | 27.69 | 360 | 0.7970 | 0.7602 | 0.7562 |
92
+ | 0.8504 | 28.46 | 370 | 0.7724 | 0.7602 | 0.7602 |
93
+ | 0.7939 | 29.23 | 380 | 0.7440 | 0.7466 | 0.7461 |
94
+ | 0.7805 | 30.0 | 390 | 0.7283 | 0.7520 | 0.7511 |
95
+ | 0.6974 | 30.77 | 400 | 0.7311 | 0.7384 | 0.7377 |
96
+ | 0.7533 | 31.54 | 410 | 0.7270 | 0.7112 | 0.6979 |
97
+ | 0.7528 | 32.31 | 420 | 0.6796 | 0.7357 | 0.7298 |
98
+ | 0.6679 | 33.08 | 430 | 0.6834 | 0.7357 | 0.7357 |
99
+ | 0.6732 | 33.85 | 440 | 0.6851 | 0.7248 | 0.7248 |
100
+ | 0.6001 | 34.62 | 450 | 0.6585 | 0.7548 | 0.7530 |
101
+ | 0.6731 | 35.38 | 460 | 0.6727 | 0.7411 | 0.7380 |
102
+ | 0.5601 | 36.15 | 470 | 0.6688 | 0.7330 | 0.7321 |
103
+ | 0.5488 | 36.92 | 480 | 0.6879 | 0.7439 | 0.7420 |
104
+ | 0.5892 | 37.69 | 490 | 0.6809 | 0.7166 | 0.7148 |
105
+ | 0.5651 | 38.46 | 500 | 0.6877 | 0.7193 | 0.7181 |
106
+ | 0.5595 | 39.23 | 510 | 0.6874 | 0.7221 | 0.7218 |
107
+ | 0.4983 | 40.0 | 520 | 0.6789 | 0.7166 | 0.7166 |
108
+ | 0.4869 | 40.77 | 530 | 0.6912 | 0.7221 | 0.7221 |
109
+ | 0.5352 | 41.54 | 540 | 0.7038 | 0.7003 | 0.6984 |
110
+ | 0.444 | 42.31 | 550 | 0.6778 | 0.7330 | 0.7321 |
111
+ | 0.5637 | 43.08 | 560 | 0.6873 | 0.6975 | 0.6973 |
112
+ | 0.5065 | 43.85 | 570 | 0.6736 | 0.7411 | 0.7405 |
113
+ | 0.4921 | 44.62 | 580 | 0.6859 | 0.7384 | 0.7381 |
114
+ | 0.4426 | 45.38 | 590 | 0.6995 | 0.7221 | 0.7221 |
115
+ | 0.4679 | 46.15 | 600 | 0.6967 | 0.7275 | 0.7271 |
116
+ | 0.6099 | 46.92 | 610 | 0.7145 | 0.6948 | 0.6947 |
117
+ | 0.4779 | 47.69 | 620 | 0.7026 | 0.7139 | 0.7139 |
118
+ | 0.4743 | 48.46 | 630 | 0.7036 | 0.7139 | 0.7137 |
119
+ | 0.4687 | 49.23 | 640 | 0.7060 | 0.7112 | 0.7112 |
120
+ | 0.459 | 50.0 | 650 | 0.7070 | 0.7112 | 0.7112 |
121
+
122
+
123
+ ### Framework versions
124
+
125
+ - Transformers 4.33.0
126
+ - Pytorch 2.0.0
127
+ - Datasets 2.1.0
128
+ - Tokenizers 0.13.3