motivbot-multilingual-mdeberta-v2

MotivBot Multilingual Mental Health Classifier — mDeBERTa v2 (Best Fold)

This model uses mDeBERTa‑v3‑base with CLS+mean pooling and structured features.
Trained on a 5‑fold ensemble; the best single fold (Fold 4) achieved a validation ternary macro‑F1 of 0.6465.
Ensemble test scores: Ternary 0.6183, Binary 0.7651.

Quick Load

import torch, json
from transformers import AutoTokenizer
from modeling import MentalHealthClassifierV6

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
with open("model_config.json") as f: config = json.load(f)
with open("preprocess_stats.json") as f: stats = json.load(f)

model = MentalHealthClassifierV6(model_name=config["model_name"])
model.load_state_dict(torch.load("pytorch_model.bin", map_location=device))
model.to(device).eval()
tokenizer = AutoTokenizer.from_pretrained(".")

# Normalise structured features using stats