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distilbert-base-uncased-finetuned-depression

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  1. README.md +19 -14
  2. model.safetensors +1 -1
  3. runs/Jun22_10-50-35_84cbb151b977/events.out.tfevents.1719053445.84cbb151b977.3689.0 +3 -0
  4. runs/Jun22_10-50-35_84cbb151b977/events.out.tfevents.1719054173.84cbb151b977.3689.1 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7232
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- - Precision: 0.6578
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- - Recall: 0.6578
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- - F1: 0.6578
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- - Accuracy: 0.6578
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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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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- - num_epochs: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 225 | 0.7378 | 0.65 | 0.65 | 0.65 | 0.65 |
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- | No log | 2.0 | 450 | 0.7232 | 0.6578 | 0.6578 | 0.6578 | 0.6578 |
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- | 0.7333 | 3.0 | 675 | 0.7565 | 0.6267 | 0.6267 | 0.6267 | 0.6267 |
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- | 0.7333 | 4.0 | 900 | 0.8141 | 0.6367 | 0.6367 | 0.6367 | 0.6367 |
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- | 0.4962 | 5.0 | 1125 | 0.8381 | 0.6378 | 0.6378 | 0.6378 | 0.6378 |
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.41.2
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  - Pytorch 2.3.0+cu121
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- - Datasets 2.19.2
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  - Tokenizers 0.19.1
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4825
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+ - Precision: 0.8928
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+ - Recall: 0.8834
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+ - F1: 0.8880
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+ - Accuracy: 0.9083
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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  - seed: 42
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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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+ - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 469 | 0.4587 | 0.9039 | 0.7100 | 0.7651 | 0.8561 |
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+ | 0.5527 | 2.0 | 938 | 0.3752 | 0.8918 | 0.8549 | 0.8710 | 0.8977 |
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+ | 0.2874 | 3.0 | 1407 | 0.4742 | 0.8652 | 0.8545 | 0.8577 | 0.8934 |
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+ | 0.1691 | 4.0 | 1876 | 0.5362 | 0.8808 | 0.8618 | 0.8694 | 0.8902 |
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+ | 0.0963 | 5.0 | 2345 | 0.4825 | 0.8928 | 0.8834 | 0.8880 | 0.9083 |
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+ | 0.0593 | 6.0 | 2814 | 0.5698 | 0.8857 | 0.8747 | 0.8790 | 0.8987 |
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+ | 0.0495 | 7.0 | 3283 | 0.6439 | 0.8962 | 0.8691 | 0.8804 | 0.8966 |
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+ | 0.037 | 8.0 | 3752 | 0.5998 | 0.8870 | 0.8771 | 0.8810 | 0.9009 |
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+ | 0.0408 | 9.0 | 4221 | 0.6265 | 0.8828 | 0.8691 | 0.8748 | 0.8955 |
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+ | 0.0299 | 10.0 | 4690 | 0.6166 | 0.8855 | 0.8721 | 0.8780 | 0.8977 |
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  ### Framework versions
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  - Transformers 4.41.2
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  - Pytorch 2.3.0+cu121
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+ - Datasets 2.20.0
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  - Tokenizers 0.19.1
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