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intent_classify

Audio Classification
Transformers
PyTorch
wav2vec2
Generated from Trainer
Eval Results (legacy)
Model card Files
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  1. README.md +204 -34
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.061946902654867256
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  ---
27
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
32
 
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.7674
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- - Accuracy: 0.0619
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  ## Model description
39
 
@@ -61,42 +61,212 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 30
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  ### Training results
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68
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
69
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 0.98 | 14 | 2.6369 | 0.1062 |
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- | No log | 1.96 | 28 | 2.6476 | 0.0973 |
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- | 2.6346 | 2.95 | 42 | 2.6571 | 0.0973 |
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- | 2.6346 | 4.0 | 57 | 2.6573 | 0.0796 |
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- | 2.6206 | 4.98 | 71 | 2.6699 | 0.0708 |
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- | 2.6206 | 5.96 | 85 | 2.6687 | 0.0619 |
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- | 2.5993 | 6.95 | 99 | 2.6739 | 0.0619 |
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- | 2.5993 | 8.0 | 114 | 2.6755 | 0.0531 |
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- | 2.5752 | 8.98 | 128 | 2.6848 | 0.0619 |
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- | 2.5752 | 9.96 | 142 | 2.6820 | 0.0354 |
80
- | 2.5487 | 10.95 | 156 | 2.6892 | 0.0354 |
81
- | 2.5487 | 12.0 | 171 | 2.6989 | 0.0442 |
82
- | 2.5112 | 12.98 | 185 | 2.7059 | 0.0354 |
83
- | 2.5112 | 13.96 | 199 | 2.7208 | 0.0442 |
84
- | 2.4728 | 14.95 | 213 | 2.7136 | 0.0442 |
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- | 2.4728 | 16.0 | 228 | 2.7208 | 0.0442 |
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- | 2.4331 | 16.98 | 242 | 2.7166 | 0.0265 |
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- | 2.4331 | 17.96 | 256 | 2.7288 | 0.0442 |
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- | 2.3926 | 18.95 | 270 | 2.7281 | 0.0354 |
89
- | 2.3926 | 20.0 | 285 | 2.7297 | 0.0531 |
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- | 2.3926 | 20.98 | 299 | 2.7471 | 0.0531 |
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- | 2.341 | 21.96 | 313 | 2.7498 | 0.0619 |
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- | 2.341 | 22.95 | 327 | 2.7535 | 0.0619 |
93
- | 2.2983 | 24.0 | 342 | 2.7534 | 0.0442 |
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- | 2.2983 | 24.98 | 356 | 2.7647 | 0.0442 |
95
- | 2.2703 | 25.96 | 370 | 2.7696 | 0.0708 |
96
- | 2.2703 | 26.95 | 384 | 2.7656 | 0.0531 |
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- | 2.238 | 28.0 | 399 | 2.7689 | 0.0619 |
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- | 2.238 | 28.98 | 413 | 2.7667 | 0.0619 |
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- | 2.213 | 29.47 | 420 | 2.7674 | 0.0619 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
100
 
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  ### Framework versions
 
22
  metrics:
23
  - name: Accuracy
24
  type: accuracy
25
+ value: 0.07058823529411765
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
32
 
33
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
34
  It achieves the following results on the evaluation set:
35
+ - Loss: 7.7654
36
+ - Accuracy: 0.0706
37
 
38
  ## Model description
39
 
 
61
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
62
  - lr_scheduler_type: linear
63
  - lr_scheduler_warmup_ratio: 0.1
64
+ - num_epochs: 200
65
 
66
  ### Training results
67
 
68
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
69
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 2.6382 | 1.0 | 15 | 2.6424 | 0.1059 |
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+ | 2.6403 | 2.0 | 30 | 2.6453 | 0.0706 |
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+ | 2.6356 | 3.0 | 45 | 2.6431 | 0.0471 |
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+ | 2.6339 | 4.0 | 60 | 2.6444 | 0.0471 |
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+ | 2.6314 | 5.0 | 75 | 2.6529 | 0.0471 |
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+ | 2.611 | 10.0 | 150 | 2.6836 | 0.0235 |
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+ | 2.5974 | 12.0 | 180 | 2.6879 | 0.0235 |
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+ | 2.5974 | 13.0 | 195 | 2.6895 | 0.0471 |
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+ | 2.5914 | 14.0 | 210 | 2.7066 | 0.0235 |
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+ | 2.567 | 16.0 | 240 | 2.7118 | 0.0471 |
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+ | 2.5579 | 17.0 | 255 | 2.7248 | 0.0471 |
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+ | 0.01 | 200.0 | 3000 | 7.7654 | 0.0706 |
270
 
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  ### Framework versions
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