Hugging Face's logo

DrishtiSharma
/
wav2vec2-base-finetuned-sentiment-mesd-v9

Audio Classification
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
1
DrishtiSharma commited on
Commit
4c98d2b
·
1 Parent(s): c9812d4

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +161 -0
README.md ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ metrics:
6
+ - accuracy
7
+ model-index:
8
+ - name: wav2vec2-base-finetuned-sentiment-mesd-v9
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
+ # wav2vec2-base-finetuned-sentiment-mesd-v9
16
+
17
+ This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
18
+ It achieves the following results on the evaluation set:
19
+ - Loss: 0.3500
20
+ - Accuracy: 0.9154
21
+
22
+ ## Model description
23
+
24
+ More information needed
25
+
26
+ ## Intended uses & limitations
27
+
28
+ More information needed
29
+
30
+ ## Training and evaluation data
31
+
32
+ More information needed
33
+
34
+ ## Training procedure
35
+
36
+ ### Training hyperparameters
37
+
38
+ The following hyperparameters were used during training:
39
+ - learning_rate: 0.0001
40
+ - train_batch_size: 64
41
+ - eval_batch_size: 40
42
+ - seed: 42
43
+ - gradient_accumulation_steps: 4
44
+ - total_train_batch_size: 256
45
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
46
+ - lr_scheduler_type: linear
47
+ - lr_scheduler_warmup_ratio: 0.01
48
+ - num_epochs: 100
49
+
50
+ ### Training results
51
+
52
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
53
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
54
+ | No log | 0.86 | 3 | 1.7825 | 0.1846 |
55
+ | 1.9553 | 1.86 | 6 | 1.7212 | 0.4308 |
56
+ | 1.9553 | 2.86 | 9 | 1.6164 | 0.3769 |
57
+ | 2.002 | 3.86 | 12 | 1.4904 | 0.3769 |
58
+ | 1.6191 | 4.86 | 15 | 1.4426 | 0.4385 |
59
+ | 1.6191 | 5.86 | 18 | 1.3516 | 0.5231 |
60
+ | 1.6209 | 6.86 | 21 | 1.2176 | 0.5538 |
61
+ | 1.6209 | 7.86 | 24 | 1.1683 | 0.5692 |
62
+ | 1.371 | 8.86 | 27 | 1.0885 | 0.5923 |
63
+ | 1.1568 | 9.86 | 30 | 1.0152 | 0.6385 |
64
+ | 1.1568 | 10.86 | 33 | 0.9289 | 0.6385 |
65
+ | 1.1023 | 11.86 | 36 | 0.9141 | 0.6308 |
66
+ | 1.1023 | 12.86 | 39 | 0.8526 | 0.6462 |
67
+ | 0.9448 | 13.86 | 42 | 0.8420 | 0.6769 |
68
+ | 0.7972 | 14.86 | 45 | 0.7976 | 0.6692 |
69
+ | 0.7972 | 15.86 | 48 | 0.8192 | 0.7308 |
70
+ | 0.7793 | 16.86 | 51 | 0.7108 | 0.7615 |
71
+ | 0.7793 | 17.86 | 54 | 0.6712 | 0.7769 |
72
+ | 0.6468 | 18.86 | 57 | 0.6684 | 0.7923 |
73
+ | 0.5083 | 19.86 | 60 | 0.6922 | 0.7385 |
74
+ | 0.5083 | 20.86 | 63 | 0.6148 | 0.7923 |
75
+ | 0.4988 | 21.86 | 66 | 0.5846 | 0.7923 |
76
+ | 0.4988 | 22.86 | 69 | 0.6050 | 0.8154 |
77
+ | 0.4123 | 23.86 | 72 | 0.5506 | 0.7846 |
78
+ | 0.3511 | 24.86 | 75 | 0.6095 | 0.7846 |
79
+ | 0.3511 | 25.86 | 78 | 0.5916 | 0.8154 |
80
+ | 0.3268 | 26.86 | 81 | 0.5912 | 0.8077 |
81
+ | 0.3268 | 27.86 | 84 | 0.5142 | 0.8538 |
82
+ | 0.3036 | 28.86 | 87 | 0.5492 | 0.8077 |
83
+ | 0.3066 | 29.86 | 90 | 0.6007 | 0.8231 |
84
+ | 0.3066 | 30.86 | 93 | 0.5748 | 0.8231 |
85
+ | 0.2538 | 31.86 | 96 | 0.6027 | 0.7692 |
86
+ | 0.2538 | 32.86 | 99 | 0.6979 | 0.7462 |
87
+ | 0.2281 | 33.86 | 102 | 0.7002 | 0.7615 |
88
+ | 0.2183 | 34.86 | 105 | 0.6650 | 0.7769 |
89
+ | 0.2183 | 35.86 | 108 | 0.5192 | 0.8462 |
90
+ | 0.2202 | 36.86 | 111 | 0.5389 | 0.8308 |
91
+ | 0.2202 | 37.86 | 114 | 0.5050 | 0.8385 |
92
+ | 0.1906 | 38.86 | 117 | 0.5722 | 0.7769 |
93
+ | 0.154 | 39.86 | 120 | 0.5239 | 0.8308 |
94
+ | 0.154 | 40.86 | 123 | 0.4448 | 0.8615 |
95
+ | 0.1474 | 41.86 | 126 | 0.4623 | 0.8615 |
96
+ | 0.1474 | 42.86 | 129 | 0.4282 | 0.8615 |
97
+ | 0.1345 | 43.86 | 132 | 0.5087 | 0.8615 |
98
+ | 0.1567 | 44.86 | 135 | 0.4859 | 0.8385 |
99
+ | 0.1567 | 45.86 | 138 | 0.6603 | 0.8077 |
100
+ | 0.1731 | 46.86 | 141 | 0.5379 | 0.8385 |
101
+ | 0.1731 | 47.86 | 144 | 0.8666 | 0.7538 |
102
+ | 0.1606 | 48.86 | 147 | 0.7518 | 0.8 |
103
+ | 0.1484 | 49.86 | 150 | 0.5986 | 0.8385 |
104
+ | 0.1484 | 50.86 | 153 | 0.6368 | 0.8231 |
105
+ | 0.2256 | 51.86 | 156 | 0.4639 | 0.8692 |
106
+ | 0.2256 | 52.86 | 159 | 0.5533 | 0.8462 |
107
+ | 0.1178 | 53.86 | 162 | 0.5038 | 0.8615 |
108
+ | 0.0815 | 54.86 | 165 | 0.5052 | 0.8692 |
109
+ | 0.0815 | 55.86 | 168 | 0.4337 | 0.8846 |
110
+ | 0.0998 | 56.86 | 171 | 0.4422 | 0.8769 |
111
+ | 0.0998 | 57.86 | 174 | 0.4317 | 0.8692 |
112
+ | 0.0855 | 58.86 | 177 | 0.4025 | 0.8923 |
113
+ | 0.0962 | 59.86 | 180 | 0.4605 | 0.8769 |
114
+ | 0.0962 | 60.86 | 183 | 0.4356 | 0.8769 |
115
+ | 0.0763 | 61.86 | 186 | 0.4614 | 0.8769 |
116
+ | 0.0763 | 62.86 | 189 | 0.4382 | 0.8846 |
117
+ | 0.0902 | 63.86 | 192 | 0.4701 | 0.8692 |
118
+ | 0.0654 | 64.86 | 195 | 0.4922 | 0.8692 |
119
+ | 0.0654 | 65.86 | 198 | 0.5413 | 0.8538 |
120
+ | 0.0651 | 66.86 | 201 | 0.5759 | 0.8615 |
121
+ | 0.0651 | 67.86 | 204 | 0.4238 | 0.9 |
122
+ | 0.0822 | 68.86 | 207 | 0.3500 | 0.9154 |
123
+ | 0.0625 | 69.86 | 210 | 0.3878 | 0.8923 |
124
+ | 0.0625 | 70.86 | 213 | 0.4952 | 0.8615 |
125
+ | 0.0548 | 71.86 | 216 | 0.4544 | 0.8615 |
126
+ | 0.0548 | 72.86 | 219 | 0.5497 | 0.8769 |
127
+ | 0.054 | 73.86 | 222 | 0.4434 | 0.8846 |
128
+ | 0.0543 | 74.86 | 225 | 0.4732 | 0.8769 |
129
+ | 0.0543 | 75.86 | 228 | 0.4425 | 0.8923 |
130
+ | 0.0881 | 76.86 | 231 | 0.4788 | 0.8769 |
131
+ | 0.0881 | 77.86 | 234 | 0.5448 | 0.8769 |
132
+ | 0.061 | 78.86 | 237 | 0.4221 | 0.9077 |
133
+ | 0.0567 | 79.86 | 240 | 0.4404 | 0.8769 |
134
+ | 0.0567 | 80.86 | 243 | 0.4099 | 0.9 |
135
+ | 0.052 | 81.86 | 246 | 0.5259 | 0.8769 |
136
+ | 0.052 | 82.86 | 249 | 0.5874 | 0.8692 |
137
+ | 0.0444 | 83.86 | 252 | 0.5555 | 0.8846 |
138
+ | 0.0332 | 84.86 | 255 | 0.5156 | 0.8615 |
139
+ | 0.0332 | 85.86 | 258 | 0.4564 | 0.8615 |
140
+ | 0.0449 | 86.86 | 261 | 0.4826 | 0.8692 |
141
+ | 0.0449 | 87.86 | 264 | 0.4726 | 0.8615 |
142
+ | 0.0385 | 88.86 | 267 | 0.4206 | 0.8846 |
143
+ | 0.0356 | 89.86 | 270 | 0.4050 | 0.8769 |
144
+ | 0.0356 | 90.86 | 273 | 0.4161 | 0.8923 |
145
+ | 0.0391 | 91.86 | 276 | 0.4100 | 0.9077 |
146
+ | 0.0391 | 92.86 | 279 | 0.4047 | 0.9 |
147
+ | 0.0249 | 93.86 | 282 | 0.4044 | 0.9 |
148
+ | 0.0399 | 94.86 | 285 | 0.3968 | 0.8846 |
149
+ | 0.0399 | 95.86 | 288 | 0.3802 | 0.9 |
150
+ | 0.031 | 96.86 | 291 | 0.3689 | 0.9 |
151
+ | 0.031 | 97.86 | 294 | 0.3616 | 0.9077 |
152
+ | 0.036 | 98.86 | 297 | 0.3584 | 0.9077 |
153
+ | 0.0386 | 99.86 | 300 | 0.3574 | 0.9077 |
154
+
155
+
156
+ ### Framework versions
157
+
158
+ - Transformers 4.17.0
159
+ - Pytorch 1.10.0+cu111
160
+ - Datasets 2.0.0
161
+ - Tokenizers 0.11.6