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---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gpt2-finetuned-depression
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-finetuned-depression
This model is a fine-tuned version of [gpt2](https://huggingface.co/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
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