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---
license: cc-by-nc-4.0
base_model: mental/mental-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mental-roberta-base-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. -->
# mental-roberta-base-finetuned-depression
This model is a fine-tuned version of [mental/mental-roberta-base](https://huggingface.co/mental/mental-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6567
- Precision: 0.8863
- Recall: 0.9168
- F1: 0.8996
- Accuracy: 0.9115
## 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 | 0.3852 | 0.7878 | 0.8253 | 0.7958 | 0.8667 |
| 0.5249 | 2.0 | 938 | 0.4720 | 0.8778 | 0.8722 | 0.8662 | 0.8913 |
| 0.2598 | 3.0 | 1407 | 0.5459 | 0.8975 | 0.8791 | 0.8865 | 0.8977 |
| 0.1624 | 4.0 | 1876 | 0.5022 | 0.9004 | 0.8979 | 0.8976 | 0.9072 |
| 0.1036 | 5.0 | 2345 | 0.6257 | 0.8910 | 0.8968 | 0.8931 | 0.9009 |
| 0.0668 | 6.0 | 2814 | 0.6531 | 0.9145 | 0.8927 | 0.9026 | 0.9104 |
| 0.0539 | 7.0 | 3283 | 0.6209 | 0.8552 | 0.9115 | 0.8802 | 0.8945 |
| 0.057 | 8.0 | 3752 | 0.6567 | 0.8863 | 0.9168 | 0.8996 | 0.9115 |
| 0.0523 | 9.0 | 4221 | 0.7184 | 0.9067 | 0.8984 | 0.8993 | 0.9083 |
| 0.0354 | 10.0 | 4690 | 0.7112 | 0.8874 | 0.9014 | 0.8914 | 0.9072 |
| 0.0268 | 11.0 | 5159 | 0.7168 | 0.8996 | 0.9012 | 0.8979 | 0.9083 |
| 0.0297 | 12.0 | 5628 | 0.7499 | 0.8667 | 0.9096 | 0.8847 | 0.9030 |
| 0.0242 | 13.0 | 6097 | 0.7554 | 0.8946 | 0.9014 | 0.8955 | 0.9072 |
| 0.0238 | 14.0 | 6566 | 0.7990 | 0.8909 | 0.9014 | 0.8934 | 0.9072 |
| 0.0178 | 15.0 | 7035 | 0.8298 | 0.8965 | 0.8933 | 0.8925 | 0.9051 |
| 0.0226 | 16.0 | 7504 | 0.8428 | 0.9099 | 0.8890 | 0.8973 | 0.9062 |
| 0.0226 | 17.0 | 7973 | 0.8490 | 0.8742 | 0.8983 | 0.8816 | 0.9041 |
| 0.0183 | 18.0 | 8442 | 0.8148 | 0.8940 | 0.8965 | 0.8930 | 0.9072 |
| 0.0188 | 19.0 | 8911 | 0.8146 | 0.8927 | 0.8960 | 0.8921 | 0.9062 |
| 0.015 | 20.0 | 9380 | 0.8216 | 0.8927 | 0.8960 | 0.8921 | 0.9062 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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