This model was developed as part of the Google Summer of Code (GSoC) 2026 project with HumanAI to support research on AI-assisted mental health crisis detection.

Model Details

Base Model: distilbert-base-uncased
Architecture: DistilBERT
Framework: Hugging Face Transformers
Task: Multi-class Text Classification
Number of Classes: 3

Risk Categories

Label	Description
High Risk	Explicit suicidal ideation, suicide planning, self-harm intent, immediate crisis
Moderate Risk	Depression, anxiety, hopelessness, emotional distress, substance abuse without explicit suicidal intent
Low Risk	General discussions, daily life experiences, emotionally neutral or non-crisis mental health posts

Dataset

The model was fine-tuned on a Reddit-based mental health dataset containing approximately 55,000+ posts collected from mental health related communities.

The dataset consists of three balanced risk categories:

High Risk
Moderate Risk
Low Risk

Posts were preprocessed using:

Text normalization
URL removal
Lowercasing
Stopword removal
Duplicate removal

Training Configuration

Parameter	Value
Learning Rate	2e-5
Batch Size	8
Epochs	3
Weight Decay	0.01
Optimizer	AdamW

Evaluation

Epoch	Training Loss	Validation Loss	Accuracy	Weighted F1
1	0.2657	0.4282	85.45%	85.32%
2	0.2367	0.5043	85.80%	85.71%
3	0.1542	0.6890	85.79%	85.69%

Best Validation Accuracy:

85.80%

Best Weighted F1 Score:

85.71%

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

MODEL_NAME = "gokulan006/distilbert-reddit-mental-health-risk-classifier"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)

text = "I don't want to live anymore."

inputs = tokenizer(
    text,
    return_tensors="pt",
    truncation=True,
    padding=True,
    max_length=128
)

with torch.no_grad():
    outputs = model(**inputs)

prediction = torch.argmax(outputs.logits, dim=1).item()

labels = {
    0: "High Risk",
    1: "Low Risk",
    2: "Moderate Risk"
}

print(labels[prediction])

Example Predictions

Input	Prediction
"I've recently started exercising and my mood has improved."	Low Risk
"I've been feeling empty for months and nothing makes me happy anymore."	Moderate Risk
"I've decided tonight will be my last night."	High Risk

Intended Use

This model is intended for:

Mental health research
NLP benchmarking
AI-assisted crisis monitoring
Educational purposes
Research prototypes

Limitations

This model does not provide medical advice.
Predictions should never be used as the sole basis for clinical or emergency decisions.
Performance may vary across different platforms, writing styles, and populations.
Human review is recommended for high-risk predictions.

Citation

If you use this model in your research, please cite:

@misc{gokulan2026distilbert,
  title={DistilBERT Reddit Mental Health Risk Classifier},
  author={Gokulan M},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/gokulan006/distilbert-reddit-mental-health-risk-classifier}
}

Acknowledgements

Developed during Google Summer of Code (GSoC) 2026 under the HumanAI organization.

Special thanks to the HumanAI mentors and the open-source NLP community for their guidance and support.

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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.2657	1.0	4790	0.4282	0.8545	0.8532
0.2367	2.0	9580	0.5043	0.8580	0.8571
0.1542	3.0	14370	0.6890	0.8579	0.8569

Framework versions

Transformers 5.12.1
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2