File size: 6,309 Bytes
e068b14
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
---
base_model: Harveenchadha/vakyansh-wav2vec2-odia-orm-100
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vakyansh-wav2vec2-odia-orm-100-audio-abuse-feature
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vakyansh-wav2vec2-odia-orm-100-audio-abuse-feature

This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-odia-orm-100](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-odia-orm-100) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7299
- Accuracy: 0.7014
- Macro F1-score: 0.6792

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------:|
| 6.7078        | 0.78  | 10   | 6.6948          | 0.0      | 0.0            |
| 6.6539        | 1.57  | 20   | 6.5580          | 0.2      | 0.0342         |
| 6.5111        | 2.35  | 30   | 6.3377          | 0.5726   | 0.3641         |
| 6.268         | 3.14  | 40   | 6.0361          | 0.5726   | 0.3641         |
| 6.0748        | 3.92  | 50   | 5.7417          | 0.5726   | 0.3641         |
| 5.8205        | 4.71  | 60   | 5.4985          | 0.5726   | 0.3641         |
| 5.6051        | 5.49  | 70   | 5.2743          | 0.5726   | 0.3641         |
| 5.3589        | 6.27  | 80   | 5.0823          | 0.5726   | 0.3641         |
| 5.2019        | 7.06  | 90   | 4.8953          | 0.5726   | 0.3641         |
| 5.0528        | 7.84  | 100  | 4.7077          | 0.5726   | 0.3641         |
| 4.868         | 8.63  | 110  | 4.5244          | 0.5726   | 0.3641         |
| 4.7081        | 9.41  | 120  | 4.3347          | 0.5726   | 0.3641         |
| 4.437         | 10.2  | 130  | 4.1455          | 0.5726   | 0.3641         |
| 4.3225        | 10.98 | 140  | 3.9551          | 0.5726   | 0.3641         |
| 4.0945        | 11.76 | 150  | 3.7694          | 0.5726   | 0.3641         |
| 4.014         | 12.55 | 160  | 3.5710          | 0.5726   | 0.3641         |
| 3.8491        | 13.33 | 170  | 3.3814          | 0.5726   | 0.3641         |
| 3.4724        | 14.12 | 180  | 3.1873          | 0.5726   | 0.3641         |
| 3.2728        | 14.9  | 190  | 2.9999          | 0.5726   | 0.3641         |
| 3.1948        | 15.69 | 200  | 2.8224          | 0.5726   | 0.3641         |
| 2.9968        | 16.47 | 210  | 2.6368          | 0.5726   | 0.3641         |
| 2.6739        | 17.25 | 220  | 2.4462          | 0.5726   | 0.3641         |
| 2.561         | 18.04 | 230  | 2.2871          | 0.5726   | 0.3641         |
| 2.5101        | 18.82 | 240  | 2.1260          | 0.5726   | 0.3641         |
| 2.3307        | 19.61 | 250  | 1.9620          | 0.5726   | 0.3641         |
| 2.1022        | 20.39 | 260  | 1.8260          | 0.5726   | 0.3641         |
| 1.9909        | 21.18 | 270  | 1.6933          | 0.5726   | 0.3641         |
| 1.766         | 21.96 | 280  | 1.5644          | 0.5726   | 0.3641         |
| 1.7143        | 22.75 | 290  | 1.4669          | 0.5726   | 0.3641         |
| 1.5073        | 23.53 | 300  | 1.3482          | 0.5726   | 0.3641         |
| 1.6055        | 24.31 | 310  | 1.2643          | 0.5726   | 0.3641         |
| 1.321         | 25.1  | 320  | 1.1930          | 0.5726   | 0.3641         |
| 1.2165        | 25.88 | 330  | 1.1128          | 0.5726   | 0.3641         |
| 1.1484        | 26.67 | 340  | 1.0493          | 0.6712   | 0.6033         |
| 1.1413        | 27.45 | 350  | 0.9925          | 0.7096   | 0.6737         |
| 1.0462        | 28.24 | 360  | 0.9471          | 0.6877   | 0.6190         |
| 0.9667        | 29.02 | 370  | 0.9209          | 0.7123   | 0.6869         |
| 0.9918        | 29.8  | 380  | 0.8892          | 0.7205   | 0.6953         |
| 0.9112        | 30.59 | 390  | 0.8414          | 0.7123   | 0.6705         |
| 0.8666        | 31.37 | 400  | 0.8291          | 0.7123   | 0.6836         |
| 0.8096        | 32.16 | 410  | 0.8284          | 0.6959   | 0.6501         |
| 0.7987        | 32.94 | 420  | 0.7729          | 0.7425   | 0.7270         |
| 0.7529        | 33.73 | 430  | 0.7542          | 0.7260   | 0.7023         |
| 0.7605        | 34.51 | 440  | 0.7535          | 0.7260   | 0.7043         |
| 0.7011        | 35.29 | 450  | 0.7882          | 0.6959   | 0.6891         |
| 0.6868        | 36.08 | 460  | 0.7378          | 0.7260   | 0.7013         |
| 0.6858        | 36.86 | 470  | 0.7518          | 0.7096   | 0.6865         |
| 0.7546        | 37.65 | 480  | 0.7163          | 0.7342   | 0.7108         |
| 0.6717        | 38.43 | 490  | 0.7158          | 0.7397   | 0.7158         |
| 0.7048        | 39.22 | 500  | 0.7755          | 0.6575   | 0.6487         |
| 0.6767        | 40.0  | 510  | 0.7469          | 0.7068   | 0.6798         |
| 0.6621        | 40.78 | 520  | 0.7166          | 0.7205   | 0.7020         |
| 0.6639        | 41.57 | 530  | 0.7143          | 0.7151   | 0.6934         |
| 0.5988        | 42.35 | 540  | 0.7547          | 0.6767   | 0.6661         |
| 0.6179        | 43.14 | 550  | 0.7394          | 0.7014   | 0.6820         |
| 0.7033        | 43.92 | 560  | 0.7312          | 0.6986   | 0.6757         |
| 0.6076        | 44.71 | 570  | 0.7331          | 0.6904   | 0.6674         |
| 0.602         | 45.49 | 580  | 0.7341          | 0.6932   | 0.6718         |
| 0.545         | 46.27 | 590  | 0.7363          | 0.6932   | 0.6738         |
| 0.5881        | 47.06 | 600  | 0.7299          | 0.7014   | 0.6792         |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3