tzoulio
/

news-bias-finder-llama-3.2-1B

@@ -12,201 +12,68 @@ tags:
 - Articles
 - Political
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.14.0

 - Articles
 - Political
 ---
+# **Llama-3.2-1B (Political Bias Detection)**
+## **Overview**
+This model is designed to detect potential political bias in news articles. Given a text passage (e.g., a news article), the model returns probabilities indicating whether the text is leaning to the *Left*, *Center*, or *Right* of the political spectrum.
+---
+## **Model Description**
+### **Model Architecture**
+- **Base Model**: [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B)
+- **Adapters**: LoRA (Low-Rank Adaptation)
+- **Precision**: 4-bit quantization enabled for efficient inference and training (with nested/double quantization).
+### **Intended Use**
+- **Primary**: Provide a text of a news article, the model outputs probabilities corresponding to three political bias labels:
+  - **LABEL_0**: Left
+  - **LABEL_1**: Center
+  - **LABEL_2**: Right
+- **Usage Scenarios**:
+  - Media research and analytics
+  - Automated or semi-automated political bias detection in digital news
+  - Educational or journalistic explorations of bias
+> **Note**: This model is *not* an authoritative arbiter of political bias. It can be used as a *supplementary* tool to help flag potential leanings.
+---
+## **How to Use**
+Below is a sample code snippet demonstrating how to load the model and apply LoRA adapters for classification:
+```python
+import torch
+import transformers
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from peft import PeftModel
+# 1. Load the *base* LLaMA model for sequence classification
+base_model_name = "meta-llama/Llama-3.2-1B"
+access_token = "YOUR_HF_ACCESS_TOKEN"  # If needed
+model = AutoModelForSequenceClassification.from_pretrained(
+    base_model_name,
+    use_auth_token=access_token,
+    num_labels=3,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+# 2. Load the LoRA adapter on top of the base model
+adapter_path = "path/to/your/lora/adapter"
+model = PeftModel.from_pretrained(model, adapter_path)
+# 3. Create the pipeline with the specified model and tokenizer
+pipeline = transformers.pipeline(
+    "text-classification",
+    model=model,
+    tokenizer=tokenizer
+)
+# Example usage
+text = "Insert the news article text here..."
+prediction = pipeline(text)
+print(prediction)