Instructions to use rbrahma2/hw1-hc3-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rbrahma2/hw1-hc3-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rbrahma2/hw1-hc3-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rbrahma2/hw1-hc3-detector") model = AutoModelForSequenceClassification.from_pretrained("rbrahma2/hw1-hc3-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
HC3 Human vs. ChatGPT Detector
This model fine-tunes sentence-transformers/all-MiniLM-L6-v2 to classify HC3 answers as human-written or ChatGPT-generated.
Results
- Frozen-embedding logistic regression baseline accuracy: 0.8449
- Fine-tuned transformer test accuracy: 0.9880
Training Data
Human ChatGPT Comparison Corpus (HC3).
Labels:
0= human1= ChatGPT
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