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distilbert-base-uncased-finetuned-depression

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distilbert
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  1. README.md +26 -16
  2. model.safetensors +1 -1
  3. runs/Jun22_11-17-16_84cbb151b977/events.out.tfevents.1719055059.84cbb151b977.3689.2 +3 -0
  4. runs/Jun22_11-17-16_84cbb151b977/events.out.tfevents.1719056846.84cbb151b977.3689.3 +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: 0.4825
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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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@@ -49,22 +49,32 @@ The following hyperparameters were used during training:
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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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  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: 0.6721
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+ - Precision: 0.9018
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+ - Recall: 0.8881
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+ - F1: 0.8946
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+ - Accuracy: 0.9168
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  ## Model description
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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: 20
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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 | 0.3914 | 0.9136 | 0.7542 | 0.8087 | 0.8678 |
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+ | 0.5449 | 2.0 | 938 | 0.3944 | 0.8652 | 0.8677 | 0.8644 | 0.8977 |
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+ | 0.2679 | 3.0 | 1407 | 0.4355 | 0.8717 | 0.8713 | 0.8703 | 0.9009 |
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+ | 0.1516 | 4.0 | 1876 | 0.4509 | 0.8757 | 0.8809 | 0.8779 | 0.9083 |
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+ | 0.0989 | 5.0 | 2345 | 0.4762 | 0.8861 | 0.8846 | 0.8854 | 0.9094 |
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+ | 0.0666 | 6.0 | 2814 | 0.4829 | 0.8878 | 0.8890 | 0.8883 | 0.9126 |
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+ | 0.0563 | 7.0 | 3283 | 0.5768 | 0.8918 | 0.8866 | 0.8885 | 0.9115 |
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+ | 0.0349 | 8.0 | 3752 | 0.6874 | 0.8898 | 0.8644 | 0.8758 | 0.8987 |
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+ | 0.0444 | 9.0 | 4221 | 0.6256 | 0.8804 | 0.8822 | 0.8790 | 0.9019 |
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+ | 0.0301 | 10.0 | 4690 | 0.6354 | 0.8897 | 0.8750 | 0.8814 | 0.9030 |
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+ | 0.0318 | 11.0 | 5159 | 0.7172 | 0.8894 | 0.8682 | 0.8770 | 0.9009 |
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+ | 0.0222 | 12.0 | 5628 | 0.6906 | 0.9001 | 0.8700 | 0.8834 | 0.9019 |
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+ | 0.0243 | 13.0 | 6097 | 0.7263 | 0.8898 | 0.8732 | 0.8800 | 0.9019 |
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+ | 0.0172 | 14.0 | 6566 | 0.6936 | 0.8945 | 0.8766 | 0.8846 | 0.9072 |
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+ | 0.0204 | 15.0 | 7035 | 0.7428 | 0.9081 | 0.8730 | 0.8889 | 0.9051 |
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+ | 0.0162 | 16.0 | 7504 | 0.7202 | 0.8966 | 0.8748 | 0.8846 | 0.9062 |
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+ | 0.0162 | 17.0 | 7973 | 0.6721 | 0.9018 | 0.8881 | 0.8946 | 0.9168 |
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+ | 0.0172 | 18.0 | 8442 | 0.7664 | 0.9037 | 0.8706 | 0.8854 | 0.9030 |
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+ | 0.0156 | 19.0 | 8911 | 0.7166 | 0.8985 | 0.8784 | 0.8876 | 0.9094 |
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+ | 0.0158 | 20.0 | 9380 | 0.7327 | 0.8966 | 0.8748 | 0.8846 | 0.9062 |
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  ### Framework versions
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