Text Classification
Transformers
Safetensors
llama
classification
bias-detection
text-embeddings-inference
Instructions to use QuixiAI/ReAligned-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuixiAI/ReAligned-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="QuixiAI/ReAligned-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("QuixiAI/ReAligned-Classifier") model = AutoModelForSequenceClassification.from_pretrained("QuixiAI/ReAligned-Classifier") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -17,6 +17,8 @@ Eric Hartford and Quixi.ai present ReAligned Classifier, a lightweight bias dete
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ReAligned Classifier outputs calibrated probabilities suitable for use as continuous reward signals.
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## Model Architecture
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- **Base Model:** meta-llama/Llama-3.2-1B
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ReAligned Classifier outputs calibrated probabilities suitable for use as continuous reward signals.
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Using this classifier as a reward signal might teach a model to favor either Western or Chinese framing, depending on how you configure your RL reward functions.
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## Model Architecture
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- **Base Model:** meta-llama/Llama-3.2-1B
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