---
license: cc-by-nc-4.0
base_model: AIMH/mental-bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BERTForDetectingDepression-Twitter2020
  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. -->

# BERTForDetectingDepression-Twitter2020

This model is a fine-tuned version of [AIMH/mental-bert-base-cased](https://huggingface.co/AIMH/mental-bert-base-cased) on data taken from [Safa, R., Bayat, P. & Moghtader, L. Automatic detection of depression symptoms in twitter using multimodal analysis. J Supercomput (2021).](https://doi.org/10.1007/s11227-021-04040-8).
It achieves the following results on the evaluation set:
- Loss: 0.8966
- Accuracy: 0.6445

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

Eval Accuracy: 0.6445

Eval Precision: 0.627281460134486

Eval Recall: 0.6690573770491803

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3.083803249747333e-05
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6484        | 1.0   | 4500 | 0.6851          | 0.637    |
| 0.5904        | 2.0   | 9000 | 0.8966          | 0.6445   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1