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distilhubert-finetuned-cry-detector

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End of training

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  1. README.md +25 -25
  2. model.safetensors +1 -1
  3. runs/Sep11_15-20-39_ubumarcos/events.out.tfevents.1726060840.ubumarcos +3 -0
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README.md CHANGED
@@ -6,9 +6,9 @@ tags:
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
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  - precision
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  - recall
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- - f1
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  model-index:
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  - name: distilhubert-finetuned-cry-detector
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  results: []
@@ -21,11 +21,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0338
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- - Accuracy: 0.9912
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- - Precision: 0.9912
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- - Recall: 0.9912
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- - F1: 0.9912
 
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  ## Model description
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@@ -45,30 +46,29 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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- - train_batch_size: 8
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- - eval_batch_size: 8
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  - seed: 123
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- - gradient_accumulation_steps: 8
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- - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- - lr_scheduler_type: cosine
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- - lr_scheduler_warmup_ratio: 0.001
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- - num_epochs: 10
 
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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- |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | No log | 0.9956 | 85 | 0.1197 | 0.9538 | 0.9535 | 0.9538 | 0.9536 |
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- | No log | 1.9912 | 170 | 0.0601 | 0.9832 | 0.9831 | 0.9832 | 0.9831 |
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- | No log | 2.9985 | 256 | 0.0506 | 0.9868 | 0.9868 | 0.9868 | 0.9868 |
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- | No log | 3.9941 | 341 | 0.0461 | 0.9861 | 0.9864 | 0.9861 | 0.9862 |
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- | No log | 4.9898 | 426 | 0.0439 | 0.9890 | 0.9891 | 0.9890 | 0.9890 |
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- | 0.0779 | 5.9971 | 512 | 0.0396 | 0.9905 | 0.9905 | 0.9905 | 0.9905 |
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- | 0.0779 | 6.9927 | 597 | 0.0350 | 0.9919 | 0.9919 | 0.9919 | 0.9919 |
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- | 0.0779 | 8.0 | 683 | 0.0335 | 0.9912 | 0.9912 | 0.9912 | 0.9912 |
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- | 0.0779 | 8.9956 | 768 | 0.0337 | 0.9912 | 0.9912 | 0.9912 | 0.9912 |
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- | 0.0779 | 9.9561 | 850 | 0.0338 | 0.9912 | 0.9912 | 0.9912 | 0.9912 |
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73
 
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  ### Framework versions
 
6
  - generated_from_trainer
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  metrics:
8
  - accuracy
9
+ - f1
10
  - precision
11
  - recall
 
12
  model-index:
13
  - name: distilhubert-finetuned-cry-detector
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  results: []
 
21
 
22
  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on an unknown dataset.
23
  It achieves the following results on the evaluation set:
24
+ - Loss: 0.2255
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+ - Accuracy: 0.9883
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+ - F1: 0.9883
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+ - Precision: 0.9883
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+ - Recall: 0.9883
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+ - Confusion Matrix: [[960, 10], [6, 389]]
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 3e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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  - seed: 123
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 128
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine_with_restarts
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 20
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+ - label_smoothing_factor: 0.1
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Confusion Matrix |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------------------:|
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+ | 0.3124 | 2.3256 | 100 | 0.2739 | 0.9641 | 0.9640 | 0.9640 | 0.9641 | [[948, 22], [27, 368]] |
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+ | 0.2337 | 4.6512 | 200 | 0.2385 | 0.9736 | 0.9737 | 0.9737 | 0.9736 | [[950, 20], [16, 379]] |
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+ | 0.2064 | 6.9767 | 300 | 0.2295 | 0.9832 | 0.9832 | 0.9832 | 0.9832 | [[958, 12], [11, 384]] |
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+ | 0.2023 | 9.3023 | 400 | 0.2277 | 0.9868 | 0.9869 | 0.9870 | 0.9868 | [[957, 13], [5, 390]] |
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+ | 0.2003 | 11.6279 | 500 | 0.2254 | 0.9875 | 0.9876 | 0.9876 | 0.9875 | [[960, 10], [7, 388]] |
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+ | 0.2002 | 13.9535 | 600 | 0.2259 | 0.9875 | 0.9876 | 0.9876 | 0.9875 | [[959, 11], [6, 389]] |
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+ | 0.1994 | 16.2791 | 700 | 0.2255 | 0.9883 | 0.9883 | 0.9883 | 0.9883 | [[960, 10], [6, 389]] |
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+ | 0.1997 | 18.6047 | 800 | 0.2254 | 0.9883 | 0.9883 | 0.9883 | 0.9883 | [[960, 10], [6, 389]] |
 
 
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  ### Framework versions
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