|
--- |
|
license: mit |
|
base_model: roberta-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: 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. --> |
|
|
|
# roberta-base-finetuned-depression |
|
|
|
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6580 |
|
- Precision: 0.8962 |
|
- Recall: 0.9023 |
|
- F1: 0.8983 |
|
- Accuracy: 0.9062 |
|
|
|
## 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: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 1.0 | 469 | 0.4520 | 0.8637 | 0.7997 | 0.8216 | 0.8635 | |
|
| 0.5796 | 2.0 | 938 | 0.6088 | 0.9063 | 0.8592 | 0.8733 | 0.8849 | |
|
| 0.3217 | 3.0 | 1407 | 0.4402 | 0.8921 | 0.8964 | 0.8936 | 0.9030 | |
|
| 0.2159 | 4.0 | 1876 | 0.6420 | 0.8725 | 0.8890 | 0.8789 | 0.8955 | |
|
| 0.1496 | 5.0 | 2345 | 0.6017 | 0.8875 | 0.8973 | 0.8908 | 0.9019 | |
|
| 0.0827 | 6.0 | 2814 | 0.6586 | 0.8804 | 0.9009 | 0.8895 | 0.9009 | |
|
| 0.0504 | 7.0 | 3283 | 0.6580 | 0.8962 | 0.9023 | 0.8983 | 0.9062 | |
|
| 0.05 | 8.0 | 3752 | 0.7374 | 0.8859 | 0.9013 | 0.8925 | 0.9030 | |
|
| 0.0394 | 9.0 | 4221 | 0.7348 | 0.8746 | 0.9043 | 0.8880 | 0.9019 | |
|
| 0.0241 | 10.0 | 4690 | 0.7371 | 0.8773 | 0.9027 | 0.8883 | 0.9030 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.2 |
|
- Pytorch 2.3.0+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|