---
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