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--- |
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license: cc-by-nc-4.0 |
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base_model: mental/mental-bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: mental-bert-base-uncased-finetuned-depression |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mental-bert-base-uncased-finetuned-depression |
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This model is a fine-tuned version of [mental/mental-bert-base-uncased](https://huggingface.co/mental/mental-bert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5358 |
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- Precision: 0.8986 |
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- Recall: 0.8885 |
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- F1: 0.8933 |
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- Accuracy: 0.9158 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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: 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 | 0.3929 | 0.8744 | 0.8346 | 0.8516 | 0.8849 | |
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| 0.4726 | 2.0 | 938 | 0.4405 | 0.9052 | 0.8359 | 0.8660 | 0.8955 | |
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| 0.2165 | 3.0 | 1407 | 0.4594 | 0.8627 | 0.8435 | 0.8515 | 0.8891 | |
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| 0.1263 | 4.0 | 1876 | 0.5213 | 0.9012 | 0.8781 | 0.8886 | 0.9094 | |
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| 0.0719 | 5.0 | 2345 | 0.4879 | 0.9036 | 0.8694 | 0.8851 | 0.9083 | |
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| 0.0471 | 6.0 | 2814 | 0.5628 | 0.9185 | 0.8639 | 0.8880 | 0.9104 | |
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| 0.0431 | 7.0 | 3283 | 0.5592 | 0.8980 | 0.8731 | 0.8846 | 0.9104 | |
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| 0.0402 | 8.0 | 3752 | 0.5948 | 0.9166 | 0.8591 | 0.8848 | 0.9094 | |
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| 0.0348 | 9.0 | 4221 | 0.5358 | 0.8986 | 0.8885 | 0.8933 | 0.9158 | |
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| 0.0276 | 10.0 | 4690 | 0.6361 | 0.9116 | 0.8619 | 0.8843 | 0.9094 | |
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| 0.0281 | 11.0 | 5159 | 0.6535 | 0.9095 | 0.8726 | 0.8897 | 0.9147 | |
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| 0.029 | 12.0 | 5628 | 0.6776 | 0.9098 | 0.8673 | 0.8868 | 0.9136 | |
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| 0.0188 | 13.0 | 6097 | 0.6940 | 0.9072 | 0.8629 | 0.8829 | 0.9072 | |
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| 0.0215 | 14.0 | 6566 | 0.7022 | 0.9168 | 0.8606 | 0.8856 | 0.9115 | |
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| 0.0184 | 15.0 | 7035 | 0.6996 | 0.9027 | 0.8687 | 0.8846 | 0.9126 | |
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| 0.0204 | 16.0 | 7504 | 0.6990 | 0.9063 | 0.8687 | 0.8861 | 0.9126 | |
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| 0.0204 | 17.0 | 7973 | 0.7268 | 0.9103 | 0.8677 | 0.8871 | 0.9115 | |
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| 0.0185 | 18.0 | 8442 | 0.7210 | 0.9066 | 0.8766 | 0.8907 | 0.9147 | |
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| 0.0181 | 19.0 | 8911 | 0.7346 | 0.9096 | 0.8732 | 0.8902 | 0.9147 | |
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| 0.0151 | 20.0 | 9380 | 0.7363 | 0.9090 | 0.8720 | 0.8892 | 0.9136 | |
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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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