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metadata
license: mit
base_model: gpt2
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: gpt2-finetuned-depression
    results: []

gpt2-finetuned-depression

This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6647
  • Precision: 0.8917
  • Recall: 0.8648
  • F1: 0.8772
  • Accuracy: 0.9104

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 469 0.4102 0.8382 0.7154 0.7590 0.8614
0.652 2.0 938 0.3335 0.8669 0.8254 0.8439 0.8838
0.3283 3.0 1407 0.4627 0.8978 0.8464 0.8689 0.9041
0.1988 4.0 1876 0.5853 0.9021 0.8324 0.8628 0.8987
0.1613 5.0 2345 0.6426 0.9034 0.8385 0.8672 0.8987
0.1013 6.0 2814 0.6247 0.8682 0.8611 0.8643 0.9041
0.0863 7.0 3283 0.7673 0.8930 0.8375 0.8617 0.8987
0.0757 8.0 3752 0.6647 0.8917 0.8648 0.8772 0.9104
0.0511 9.0 4221 0.6658 0.8768 0.8674 0.8720 0.9030
0.0581 10.0 4690 0.7686 0.9104 0.8595 0.8824 0.9094
0.0311 11.0 5159 0.6830 0.8918 0.8488 0.8685 0.8977
0.0537 12.0 5628 0.7438 0.9078 0.8563 0.8795 0.9062
0.0436 13.0 6097 0.7950 0.8933 0.8438 0.8663 0.8987
0.042 14.0 6566 0.7248 0.8986 0.8507 0.8726 0.9030
0.0374 15.0 7035 0.6973 0.8884 0.8504 0.8681 0.9009
0.0371 16.0 7504 0.7294 0.8874 0.8554 0.8703 0.9030
0.0371 17.0 7973 0.7649 0.8937 0.8486 0.8692 0.9030
0.0318 18.0 8442 0.7576 0.8879 0.8467 0.8657 0.9009
0.0307 19.0 8911 0.7556 0.8937 0.8486 0.8692 0.9030
0.0264 20.0 9380 0.7647 0.8930 0.8486 0.8689 0.9030

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1