This model is a QLoRA (Quantized LoRA) fine-tuned version of Qwen/Qwen3-Embedding-0.6B specifically trained to identify constructive conversations in online discussion threads. The model was trained using self-training techniques on Reddit discussion data.

Model Type: Text Classification (Binary)
Base Model: Qwen/Qwen3-Embedding-0.6B
Training Method: QLoRA with self-training
Task: Binary classification of conversation constructiveness
Language: English

Model Source

Repository: https://github.com/Niklas257/Reddit-Constructiveness-Classification.git

Intended Uses

Primary Use Case

Classifying Reddit discussions as constructive or non-constructive
Content moderation assistance
Conversation quality analysis
Social media research

Direct Use

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel
import torch

# Load base model and tokenizer
base_model_name = "Qwen/Qwen3-Embedding-0.6B"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = AutoModelForSequenceClassification.from_pretrained(
    base_model_name,
    num_labels=2
)

# Load the fine-tuned adapters
model = PeftModel.from_pretrained(model, "NiklasKoch/qwen-discussion-classifier")
model.eval()

# Classify text
def classify_text(text):
    inputs = tokenizer(
        text, 
        return_tensors="pt", 
        truncation=True, 
        padding=True, 
        max_length=4096
    )
    
    # Move inputs to same device as model (important for GPU usage)
    inputs = {k: v.to(next(model.parameters()).device) for k, v in inputs.items()}
    
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        
    # 0 = non-constructive, 1 = constructive
    predicted_class = torch.argmax(predictions, dim=-1).item()
    confidence = predictions[0][predicted_class].item()
    
    return {
        'class': 'constructive' if predicted_class == 1 else 'non-constructive',
        'confidence': confidence,
        'scores': {
            'non-constructive': predictions[0][0].item(),
            'constructive': predictions[0][1].item()
        }
    }

# Example usage
text = "[author0] LEGO: What do you think you're doing?!? [author1] I don't get it did he reveal bionicle reboot or smthn? [author2] Not really, he did announce something but was super vague, seems like a sort of passion project we wants to do with the community, he even said it might not even be bionicle. [author1] So is that image fan made or is it one of his passion projects [author2] Those pictures are real and on his insta, he did a stream talking about it I\u2019m sure you can find somewhere, search up Fabre bionicle stream 2020 or something. [author1] OK thanks"
result = classify_text(text)
print(result)

Training Details

Training Data

Source: https://archive.org/download/pushshift_reddit_200506_to_202212/
Size: The dataset I used contained a total of ~1.4 million Reddit threads filtered for English language and a minimum of 2 authors per thread.
Labels: Binary (constructive/non-constructive conversations)
Additional Data: YNACC and IAC datasets for initial supervised training

Training Procedure

Training Method: Self-Training
Quantization: 4-bit QLoRA
LoRA Config:
- r: 16
- lora_alpha: 32
- lora_dropout: 0.1
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Loss Function: Focal Loss with class weighting
Max Sequence Length: 4096 tokens
Batch Size: 64
Learning Rate: 2e-6

Training Hardware

48 hours on 4x NVIDIA A100 40GB GPUs

Performance

Evaluation Results

YNACC:
Accuracy: 0.70
Precision: 0.72
F1-Score: 0.69

IAC:
Accuracy: 0.78
Precision: 0.86
F1-Score: 0.86

Reddit:
Accuracy: 0.64
Precision: 0.76
F1-Score: 0.74

Limitations and Bias

Language: English only
Bias: May reflect biases present in Reddit discussions and training data

Ethical Considerations

Human oversight is recommended for important moderation decisions

Technical Specifications

Model Architecture: Qwen 3 Embedding + Classification Head
Parameters: ~600M base + LoRA adapters + classification head
Precision: 4-bit quantized base model with full-precision adapters
Framework: PyTorch, Transformers, PEFT (any recent version - you may see harmless warnings about configuration parameters)

Model Card Authors

Niklas Koch, Georg August University of Göttingen

Model Card Contact

niklas.koch01@stud.uni-goettingen.de