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indrajitharidas
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1.0.0

Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
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  1. README.md +26 -9
  2. model.safetensors +1 -1
  3. runs/Sep26_11-03-02_Indrajits-MBP/events.out.tfevents.1727373786.Indrajits-MBP.17655.0 +2 -2
README.md CHANGED
@@ -25,13 +25,13 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: 0.11428571428571428
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  - name: Precision
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  type: precision
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  value: 0.6666666666666666
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  - name: Recall
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  type: recall
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- value: 0.0625
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,10 +41,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6934
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- - F1: 0.1143
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  - Precision: 0.6667
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- - Recall: 0.0625
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  ## Model description
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@@ -72,15 +72,32 @@ 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: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
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- | No log | 1.0 | 2 | 0.6925 | 0.1176 | 1.0 | 0.0625 |
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- | No log | 2.0 | 4 | 0.6929 | 0.1081 | 0.4 | 0.0625 |
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- | No log | 3.0 | 6 | 0.6934 | 0.1143 | 0.6667 | 0.0625 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: F1
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  type: f1
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+ value: 0.21052631578947367
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  - name: Precision
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  type: precision
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  value: 0.6666666666666666
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  - name: Recall
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  type: recall
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+ value: 0.125
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6981
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+ - F1: 0.2105
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  - Precision: 0.6667
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+ - Recall: 0.125
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  ## Model description
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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: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
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+ | No log | 1.0 | 2 | 0.6927 | 0.2857 | 0.6 | 0.1875 |
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+ | No log | 2.0 | 4 | 0.6922 | 0.1143 | 0.6667 | 0.0625 |
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+ | No log | 3.0 | 6 | 0.6928 | 0.0606 | 1.0 | 0.0312 |
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+ | No log | 4.0 | 8 | 0.6914 | 0.1176 | 1.0 | 0.0625 |
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+ | 0.6903 | 5.0 | 10 | 0.6931 | 0.1176 | 1.0 | 0.0625 |
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+ | 0.6903 | 6.0 | 12 | 0.6942 | 0.1176 | 1.0 | 0.0625 |
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+ | 0.6903 | 7.0 | 14 | 0.6947 | 0.1081 | 0.4 | 0.0625 |
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+ | 0.6903 | 8.0 | 16 | 0.6945 | 0.1143 | 0.6667 | 0.0625 |
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+ | 0.6903 | 9.0 | 18 | 0.6955 | 0.1111 | 0.5 | 0.0625 |
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+ | 0.6811 | 10.0 | 20 | 0.6974 | 0.1111 | 0.5 | 0.0625 |
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+ | 0.6811 | 11.0 | 22 | 0.6991 | 0.0571 | 0.3333 | 0.0312 |
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+ | 0.6811 | 12.0 | 24 | 0.7006 | 0.1111 | 0.5 | 0.0625 |
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+ | 0.6811 | 13.0 | 26 | 0.7007 | 0.1111 | 0.5 | 0.0625 |
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+ | 0.6811 | 14.0 | 28 | 0.7013 | 0.1111 | 0.5 | 0.0625 |
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+ | 0.6745 | 15.0 | 30 | 0.7015 | 0.1111 | 0.5 | 0.0625 |
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+ | 0.6745 | 16.0 | 32 | 0.7004 | 0.1111 | 0.5 | 0.0625 |
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+ | 0.6745 | 17.0 | 34 | 0.6992 | 0.1622 | 0.6 | 0.0938 |
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+ | 0.6745 | 18.0 | 36 | 0.6990 | 0.1622 | 0.6 | 0.0938 |
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+ | 0.6745 | 19.0 | 38 | 0.6986 | 0.2105 | 0.6667 | 0.125 |
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+ | 0.6669 | 20.0 | 40 | 0.6981 | 0.2105 | 0.6667 | 0.125 |
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  ### Framework versions
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