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--- |
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license: cc-by-nc-4.0 |
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base_model: mental/mental-roberta-base |
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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-roberta-base-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-roberta-base-finetuned-depression |
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This model is a fine-tuned version of [mental/mental-roberta-base](https://huggingface.co/mental/mental-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6567 |
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- Precision: 0.8863 |
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- Recall: 0.9168 |
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- F1: 0.8996 |
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- Accuracy: 0.9115 |
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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 | 0.3852 | 0.7878 | 0.8253 | 0.7958 | 0.8667 | |
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| 0.5249 | 2.0 | 938 | 0.4720 | 0.8778 | 0.8722 | 0.8662 | 0.8913 | |
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| 0.2598 | 3.0 | 1407 | 0.5459 | 0.8975 | 0.8791 | 0.8865 | 0.8977 | |
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| 0.1624 | 4.0 | 1876 | 0.5022 | 0.9004 | 0.8979 | 0.8976 | 0.9072 | |
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| 0.1036 | 5.0 | 2345 | 0.6257 | 0.8910 | 0.8968 | 0.8931 | 0.9009 | |
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| 0.0668 | 6.0 | 2814 | 0.6531 | 0.9145 | 0.8927 | 0.9026 | 0.9104 | |
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| 0.0539 | 7.0 | 3283 | 0.6209 | 0.8552 | 0.9115 | 0.8802 | 0.8945 | |
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| 0.057 | 8.0 | 3752 | 0.6567 | 0.8863 | 0.9168 | 0.8996 | 0.9115 | |
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| 0.0523 | 9.0 | 4221 | 0.7184 | 0.9067 | 0.8984 | 0.8993 | 0.9083 | |
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| 0.0354 | 10.0 | 4690 | 0.7112 | 0.8874 | 0.9014 | 0.8914 | 0.9072 | |
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| 0.0268 | 11.0 | 5159 | 0.7168 | 0.8996 | 0.9012 | 0.8979 | 0.9083 | |
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| 0.0297 | 12.0 | 5628 | 0.7499 | 0.8667 | 0.9096 | 0.8847 | 0.9030 | |
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| 0.0242 | 13.0 | 6097 | 0.7554 | 0.8946 | 0.9014 | 0.8955 | 0.9072 | |
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| 0.0238 | 14.0 | 6566 | 0.7990 | 0.8909 | 0.9014 | 0.8934 | 0.9072 | |
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| 0.0178 | 15.0 | 7035 | 0.8298 | 0.8965 | 0.8933 | 0.8925 | 0.9051 | |
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| 0.0226 | 16.0 | 7504 | 0.8428 | 0.9099 | 0.8890 | 0.8973 | 0.9062 | |
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| 0.0226 | 17.0 | 7973 | 0.8490 | 0.8742 | 0.8983 | 0.8816 | 0.9041 | |
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| 0.0183 | 18.0 | 8442 | 0.8148 | 0.8940 | 0.8965 | 0.8930 | 0.9072 | |
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| 0.0188 | 19.0 | 8911 | 0.8146 | 0.8927 | 0.8960 | 0.8921 | 0.9062 | |
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| 0.015 | 20.0 | 9380 | 0.8216 | 0.8927 | 0.8960 | 0.8921 | 0.9062 | |
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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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