Hello-SimpleAI/HC3
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How to use radagon213/hw1-hc3-detector with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="radagon213/hw1-hc3-detector") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("radagon213/hw1-hc3-detector")
model = AutoModelForSequenceClassification.from_pretrained("radagon213/hw1-hc3-detector", device_map="auto")sentence-transformers/all-MiniLM-L6-v2 fine-tuned end-to-end (AutoModelForSequenceClassification, 2 labels)
on the English HC3 corpus (revision 4d0ff18143b5a7e1b1e79beb540c04549d1e59d3) to classify answers as
0 = human or 1 = ChatGPT.
| Model | Test accuracy |
|---|---|
| Baseline: frozen all-MiniLM-L6-v2 embeddings + logistic regression | 0.8449 |
| Fine-tuned (this model) | 0.9949 |
HC3 is a historical benchmark (Dec 2022 ChatGPT). This model is not a reliable detector of text from current LLMs or of student work.
Base model
nreimers/MiniLM-L6-H384-uncased