Instructions to use Gabalo/hw1-hc3-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gabalo/hw1-hc3-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gabalo/hw1-hc3-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gabalo/hw1-hc3-detector") model = AutoModelForSequenceClassification.from_pretrained("Gabalo/hw1-hc3-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
Results
| Model | Test Accuracy |
|---|---|
| Frozen sentence-embedding baseline (Logistic Regression) | 0.8449 |
Fine-tuned sentence-transformers/all-MiniLM-L6-v2 classifier |
0.9970 |
The fine-tuned model was trained on the HC3 human-vs.-ChatGPT answer classification task. Results were evaluated on the held-out test split.
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