Hugging Face's logo

callmesan
/
vakyansh-wav2vec2-kannada-knm-560-audio-abuse-feature

Audio Classification
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Community
callmesan commited on
Commit
0b4b083
•
1 Parent(s): 4e4d821

End of training

Browse files
Files changed (1) hide show
  1. README.md +127 -0
README.md ADDED
@@ -0,0 +1,127 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: Harveenchadha/vakyansh-wav2vec2-kannada-knm-560
3
+ tags:
4
+ - generated_from_trainer
5
+ metrics:
6
+ - accuracy
7
+ model-index:
8
+ - name: vakyansh-wav2vec2-kannada-knm-560-audio-abuse-feature
9
+ results: []
10
+ ---
11
+
12
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
+ should probably proofread and complete it, then remove this comment. -->
14
+
15
+ # vakyansh-wav2vec2-kannada-knm-560-audio-abuse-feature
16
+
17
+ This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-kannada-knm-560](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-kannada-knm-560) on the None dataset.
18
+ It achieves the following results on the evaluation set:
19
+ - Loss: 0.6403
20
+ - Accuracy: 0.7100
21
+ - Macro F1-score: 0.6596
22
+
23
+ ## Model description
24
+
25
+ More information needed
26
+
27
+ ## Intended uses & limitations
28
+
29
+ More information needed
30
+
31
+ ## Training and evaluation data
32
+
33
+ More information needed
34
+
35
+ ## Training procedure
36
+
37
+ ### Training hyperparameters
38
+
39
+ The following hyperparameters were used during training:
40
+ - learning_rate: 2e-05
41
+ - train_batch_size: 16
42
+ - eval_batch_size: 16
43
+ - seed: 42
44
+ - gradient_accumulation_steps: 4
45
+ - total_train_batch_size: 64
46
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
47
+ - lr_scheduler_type: linear
48
+ - lr_scheduler_warmup_ratio: 0.1
49
+ - num_epochs: 50
50
+
51
+ ### Training results
52
+
53
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1-score |
54
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------:|
55
+ | 6.6756 | 0.77 | 10 | 6.6487 | 0.0 | 0.0 |
56
+ | 6.6336 | 1.54 | 20 | 6.5448 | 0.5474 | 0.0647 |
57
+ | 6.4999 | 2.31 | 30 | 6.3245 | 0.6585 | 0.3971 |
58
+ | 6.2688 | 3.08 | 40 | 6.0120 | 0.6585 | 0.3971 |
59
+ | 6.0598 | 3.85 | 50 | 5.7401 | 0.6585 | 0.3971 |
60
+ | 5.7739 | 4.62 | 60 | 5.4859 | 0.6585 | 0.3971 |
61
+ | 5.5736 | 5.38 | 70 | 5.2443 | 0.6585 | 0.3971 |
62
+ | 5.3092 | 6.15 | 80 | 5.0361 | 0.6585 | 0.3971 |
63
+ | 5.1088 | 6.92 | 90 | 4.8282 | 0.6585 | 0.3971 |
64
+ | 4.9566 | 7.69 | 100 | 4.6295 | 0.6585 | 0.3971 |
65
+ | 4.7528 | 8.46 | 110 | 4.4350 | 0.6585 | 0.3971 |
66
+ | 4.6942 | 9.23 | 120 | 4.2479 | 0.6585 | 0.3971 |
67
+ | 4.4164 | 10.0 | 130 | 4.0578 | 0.6585 | 0.3971 |
68
+ | 4.1989 | 10.77 | 140 | 3.8571 | 0.6585 | 0.3971 |
69
+ | 4.0312 | 11.54 | 150 | 3.6581 | 0.6585 | 0.3971 |
70
+ | 3.8758 | 12.31 | 160 | 3.4561 | 0.6585 | 0.3971 |
71
+ | 3.7026 | 13.08 | 170 | 3.2569 | 0.6585 | 0.3971 |
72
+ | 3.4173 | 13.85 | 180 | 3.0592 | 0.6585 | 0.3971 |
73
+ | 3.2018 | 14.62 | 190 | 2.8633 | 0.6585 | 0.3971 |
74
+ | 3.1789 | 15.38 | 200 | 2.6746 | 0.6585 | 0.3971 |
75
+ | 2.8636 | 16.15 | 210 | 2.4860 | 0.6585 | 0.3971 |
76
+ | 2.6381 | 16.92 | 220 | 2.3059 | 0.6585 | 0.3971 |
77
+ | 2.5071 | 17.69 | 230 | 2.1303 | 0.6585 | 0.3971 |
78
+ | 2.2478 | 18.46 | 240 | 1.9669 | 0.6585 | 0.3971 |
79
+ | 2.2718 | 19.23 | 250 | 1.8162 | 0.6585 | 0.3971 |
80
+ | 2.0259 | 20.0 | 260 | 1.6750 | 0.6585 | 0.3971 |
81
+ | 1.8823 | 20.77 | 270 | 1.5460 | 0.6585 | 0.3971 |
82
+ | 1.6591 | 21.54 | 280 | 1.4290 | 0.6585 | 0.3971 |
83
+ | 1.5646 | 22.31 | 290 | 1.3213 | 0.6585 | 0.3971 |
84
+ | 1.487 | 23.08 | 300 | 1.2263 | 0.6585 | 0.3971 |
85
+ | 1.3681 | 23.85 | 310 | 1.1424 | 0.6585 | 0.3971 |
86
+ | 1.2941 | 24.62 | 320 | 1.0696 | 0.6585 | 0.3971 |
87
+ | 1.1374 | 25.38 | 330 | 1.0059 | 0.6585 | 0.3971 |
88
+ | 1.0881 | 26.15 | 340 | 0.9470 | 0.6585 | 0.3971 |
89
+ | 0.9892 | 26.92 | 350 | 0.8987 | 0.6585 | 0.3971 |
90
+ | 1.0156 | 27.69 | 360 | 0.8547 | 0.6585 | 0.3971 |
91
+ | 0.9592 | 28.46 | 370 | 0.8181 | 0.6585 | 0.3971 |
92
+ | 0.937 | 29.23 | 380 | 0.7861 | 0.6585 | 0.3971 |
93
+ | 0.8938 | 30.0 | 390 | 0.7572 | 0.6585 | 0.3971 |
94
+ | 0.8651 | 30.77 | 400 | 0.7331 | 0.6585 | 0.3971 |
95
+ | 0.8051 | 31.54 | 410 | 0.7182 | 0.6585 | 0.3971 |
96
+ | 0.7774 | 32.31 | 420 | 0.7072 | 0.6585 | 0.3971 |
97
+ | 0.749 | 33.08 | 430 | 0.6787 | 0.6585 | 0.3971 |
98
+ | 0.7762 | 33.85 | 440 | 0.6687 | 0.6585 | 0.3971 |
99
+ | 0.7223 | 34.62 | 450 | 0.6656 | 0.7480 | 0.6544 |
100
+ | 0.7363 | 35.38 | 460 | 0.6619 | 0.7534 | 0.6963 |
101
+ | 0.7039 | 36.15 | 470 | 0.6473 | 0.7371 | 0.6867 |
102
+ | 0.6923 | 36.92 | 480 | 0.6377 | 0.7453 | 0.6854 |
103
+ | 0.6667 | 37.69 | 490 | 0.6405 | 0.7317 | 0.6786 |
104
+ | 0.6419 | 38.46 | 500 | 0.6479 | 0.7127 | 0.6794 |
105
+ | 0.6511 | 39.23 | 510 | 0.6336 | 0.7344 | 0.6757 |
106
+ | 0.6638 | 40.0 | 520 | 0.6244 | 0.7236 | 0.6927 |
107
+ | 0.67 | 40.77 | 530 | 0.6241 | 0.7290 | 0.6795 |
108
+ | 0.616 | 41.54 | 540 | 0.6353 | 0.7182 | 0.6789 |
109
+ | 0.6592 | 42.31 | 550 | 0.6277 | 0.7344 | 0.6890 |
110
+ | 0.6146 | 43.08 | 560 | 0.6352 | 0.7236 | 0.6890 |
111
+ | 0.6103 | 43.85 | 570 | 0.6382 | 0.7100 | 0.6629 |
112
+ | 0.6099 | 44.62 | 580 | 0.6373 | 0.7100 | 0.6629 |
113
+ | 0.5724 | 45.38 | 590 | 0.6358 | 0.7182 | 0.6667 |
114
+ | 0.6134 | 46.15 | 600 | 0.6410 | 0.7073 | 0.6680 |
115
+ | 0.6084 | 46.92 | 610 | 0.6441 | 0.7127 | 0.6755 |
116
+ | 0.656 | 47.69 | 620 | 0.6400 | 0.7127 | 0.6727 |
117
+ | 0.6359 | 48.46 | 630 | 0.6405 | 0.7100 | 0.6689 |
118
+ | 0.5832 | 49.23 | 640 | 0.6407 | 0.7073 | 0.6621 |
119
+ | 0.5822 | 50.0 | 650 | 0.6403 | 0.7100 | 0.6596 |
120
+
121
+
122
+ ### Framework versions
123
+
124
+ - Transformers 4.33.0
125
+ - Pytorch 2.0.0
126
+ - Datasets 2.1.0
127
+ - Tokenizers 0.13.3