SrothJr/banglamentalBERT-sahajBERT-dapt

banglamentalBERRT on sahajBERT(DAPT) - Depression Severity Detection

This is a Domain-Adaptive Pre-Trained (DAPT) model for detecting depression severity in Bangla social media text. A lightweight, efficient model based on ALBERT. optimized for low-resource environments.

Model Details

• —Base Architecture: neuropark/sahajBERT
• —Training Method: Domain-Adaptive Pre-Training (DAPT) on bangla mental health corpora, followed by Fine-Tuning.
• —Task: Multi-class Classification (4 classes).
• —Language: Bengali (Bangla).

Label Mapping

The model outputs one of the following classes:

• —0: Minimum/None
• —1: Mild
• —2: Moderate
• —3: Severe

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load the model
model_name = "SrothJr/banglamentalBERT-sahajBERT-dapt"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Inference
text = "আপনার বাংলা টেক্সট এখানে (Your Bengali text here)"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)
    
predictions = torch.argmax(outputs.logits, dim=-1)
labels = ["Minimum", "Mild", "Moderate", "Severe"]
print(f"Prediction: {labels[predictions.item()]}")

Citation

If you use this mode, please cite our research.