mrjunos/depression-reddit-distilroberta-base

Example Pipeline

from transformers import pipeline
predict_task = pipeline(model="mrjunos/depression-reddit-distilroberta-base", task="text-classification")
predict_task("Stop listing your issues here, use forum instead or open ticket.")

[{'label': 'not_depression', 'score': 0.9813856482505798}]

Disclaimer: This machine learning model classifies texts related to depression, but I am not an expert or a mental health professional. I do not intend to diagnose or offer medical advice. The information provided should not replace consultation with a qualified professional. The results may not be accurate. Use this model at your own risk and seek professional advice if needed.

This model is a fine-tuned version of distilroberta-base on the mrjunos/depression-reddit-cleaned dataset. It achieves the following results on the evaluation set:

• Loss: 0.0821
• Accuracy: 0.9716

Model description

This model is a transformer-based model that has been fine-tuned on a dataset of Reddit posts related to depression. The model can be used to classify posts as either depression or not depression.

Intended uses & limitations

This model is intended to be used for research purposes. It is not yet ready for production use. The model has been trained on a dataset of English-language posts, so it may not be accurate for other languages.

Training and evaluation data

The model was trained on the mrjunos/depression-reddit-cleaned dataset, which contains approximately 7,000 labeled instances. The data was split into Train and Test using:

ds = ds['train'].train_test_split(test_size=0.2, seed=42)

The dataset consists of two main features: 'text' and 'label'. The 'text' feature contains the text data from Reddit posts related to depression, while the 'label' feature indicates whether a post is classified as depression or not.

Training procedure

You can find here the steps I followed to train this model: https://github.com/mrjunos/machine_learning/blob/main/NLP-fine_tunning-hugging_face_model.ipynb

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1711	0.65	500	0.0821	0.9716
0.1022	1.29	1000	0.1148	0.9709
0.0595	1.94	1500	0.1178	0.9787
0.0348	2.59	2000	0.0951	0.9851

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.0
• Tokenizers 0.13.3