|
|
--- |
|
|
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 |
|
|
|
