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license: mit
language:
- en
tags:
- roberta
- text-classification
- ensemble
- clarity
- qevasion
pipeline_tag: text-classification
---
# 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
```python
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.
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