gigibot/ensemble-qeval

RoBERTa Clarity Ensemble

This repository contains 3 RoBERTa-large models fine-tuned for clarity classification (Clear Reply / Clear Non-Reply / Ambivalent).

Models

Model	Description
model-1/	RoBERTa-large fine-tuned on clarity task
model-2/	RoBERTa-large fine-tuned on clarity task (different seed/split)
model-3/	RoBERTa-large fine-tuned on clarity task (different seed/split)

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load one model
model = AutoModelForSequenceClassification.from_pretrained("gigibot/ensemble-qeval", subfolder="model-1")
tokenizer = AutoTokenizer.from_pretrained("gigibot/ensemble-qeval", subfolder="model-1")

# Or load all 3 for ensemble voting
models = []
for i in [1, 2, 3]:
    m = AutoModelForSequenceClassification.from_pretrained(
        "gigibot/ensemble-qeval",
        subfolder=f"model-{{i}}"
    )
    models.append(m)

# Ensemble inference
def ensemble_predict(text, models, tokenizer):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
    logits_sum = None
    for model in models:
        model.eval()
        with torch.no_grad():
            out = model(**inputs)
            if logits_sum is None:
                logits_sum = out.logits
            else:
                logits_sum += out.logits
    return torch.argmax(logits_sum, dim=-1).item()

Labels

• 0: Clear Reply
• 1: Clear Non-Reply
• 2: Ambivalent

Training

Each model was fine-tuned from roberta-large on the QEvasion clarity dataset.