qingy2024 / HQRD-67M

HQRD 67M (fine tuned from distilbert-base-uncased)

This is a 67M parameter model fine-tuned to detect high quality responses. It outputs a score ranging from 0 (bad) to 1 (good). However, occasionally it can output a value slightly outside of that range, such as 1.01 or -0.013

Example Inference Code

from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load the model and tokenizer from the Hugging Face Hub
model_name = "qingy2024/HQRD-67M"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example text to classify
text = "Quantum mechanics is a fundamental branch of physics that describes the behavior of particles on very small scales, such as atoms and subatomic particles. It differs significantly from classical mechanics, which governs macroscopic objects, because it introduces concepts like wave-particle duality, uncertainty, and probabilistic outcomes."

# Tokenize the text
inputs = tokenizer(text, truncation=True, max_length=512, padding=True, return_tensors="pt")

import torch
# Perform inference
with torch.no_grad():  # Disable gradient computation for inference
    outputs = model(**inputs)
    prediction = outputs.logits.item()  # Extract the single float value

# Interpret the result
print(f"Prediction score: {prediction:.3f}")