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README.md

@@ -16,32 +16,86 @@ language:
 pipeline_tag: text-generation
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-# CES Phase 3B LoRA: With Party ID
+# CES Phase 3B LoRA: With Party Identification
 
-LoRA adapter that includes party identification as an input variable. **For most use cases, prefer [Phase 3A](https://huggingface.co/baglecake/ces-phase3a-lora) instead.**
+A LoRA adapter for Llama 3.1 8B Instruct that predicts political ideology using party identification in addition to leader ratings and policy positions.
+
+**For most use cases, prefer [Phase 3A](https://huggingface.co/baglecake/ces-phase3a-lora) instead** — this model exists to demonstrate that party ID is redundant.
+
+## Model Description
+
+This model was trained on the Canadian Election Study (CES) 2021 to predict self-reported ideology (0-10 left-right scale) from:
+
+- **Demographics**: Age, gender, province, education, employment, religion, marital status, urban/rural, born in Canada
+- **Leader Thermometers**: Ratings (0-100) of Justin Trudeau, Erin O'Toole, and Jagmeet Singh
+- **Wedge Issues**: Positions on carbon tax, energy/pipelines, and medical assistance in dying (MAID)
+- **Government Satisfaction**: Overall satisfaction with federal government
+- **Party Identification**: "I usually think of myself as a Liberal/Conservative/NDP..." (ONLY IN THIS MODEL)
 
 ## Performance
 
-| Model | Variables | r |
-|-------|-----------|---|
-| Phase 3A | Demographics + Leader Ratings + Wedge Issues | 0.560 |
-| **Phase 3B (this model)** | Same + Party ID | **0.574** |
+| Model | Inputs | Correlation (r) |
+|-------|--------|-----------------|
+| Phase 2 | Demographics + 3 psychographics | 0.428 |
+| Phase 3A | + Leader thermometers + wedge issues | 0.560 |
+| **Phase 3B (this model)** | **+ Party ID** | **0.574** |
 
-**Partisan Delta = 0.014** (essentially zero)
+**Partisan Delta = 0.014** — Party ID adds only 1.4% improvement.
 
 ## Why Phase 3A is Preferred
 
-Adding party ID only improves correlation by 1.4%. This proves party identity is **redundant** — it's already encoded in leader affect and policy positions.
+We trained this model (Phase 3B) specifically to test whether party identification adds predictive value beyond substantive attitudes. It doesn't.
+
+**The null result is the finding:**
+- Party identity is **redundant** — it's already encoded in how people feel about leaders and their policy positions
+- Canadian ideology is **substantive, not tribal** — people's "team" reflects their actual views
+- Adding party ID is "cheating" — you're just asking people their ideology with extra steps
+
+Phase 3B exists for reproducibility and to demonstrate this null result empirically.
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-Phase 3B exists for reproducibility and to demonstrate this null result.
+base_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Meta-Llama-3.1-8B-Instruct",
+    load_in_4bit=True
+)
+model = PeftModel.from_pretrained(base_model, "baglecake/ces-phase3b-lora")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
+
+# Example prompt (note: includes party ID)
+system = """You are a 45-year-old man from Ontario, Canada. You live in a suburb of a large city. Your highest level of education is a bachelor's degree. You are currently employed full-time. You are married. You have children. You are Catholic. You were born in Canada.
+
+Political Profile:
+Leader Ratings: Justin Trudeau: 25/100, Erin O'Toole: 70/100, Jagmeet Singh: 30/100.
+Views: Strongly disagrees that the federal government should continue the carbon tax; strongly agrees that the government should do more to help the energy sector/pipelines.
+Overall Satisfaction: Is not at all satisfied with the federal government.
+Party ID: Generally thinks of themselves as a Conservative.
+
+Answer survey questions as this person would, based on their background and detailed political profile."""
+
+user = "On a scale from 0 to 10, where 0 means left/liberal and 10 means right/conservative, where would you place yourself politically? Just give the number."
+```
 
 ## Training Details
 
 - **Base model**: meta-llama/Meta-Llama-3.1-8B-Instruct (4-bit quantized via Unsloth)
-- **Training data**: ~14,455 examples from CES 2021
+- **Training data**: 14,455 examples from CES 2021
 - **LoRA rank**: 32
 - **LoRA alpha**: 64
+- **Target modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
 - **Epochs**: 3
+- **Hardware**: NVIDIA A100 40GB (Colab Pro)
+
+## Limitations
+
+1. **Narrow task**: Model only outputs ideology numbers (0-10).
+2. **Canadian-specific**: Trained on CES 2021 under Trudeau government.
+3. **Leader-specific**: Uses 2021 leader names.
+4. **Redundant information**: Party ID doesn't add meaningful predictive value over Phase 3A.
 
 ## Citation