gpt genereated readme :)

README.md

@@ -7,18 +7,18 @@ tags:
   - fine-tuning
 license: mit
 datasets:
-  - your-dataset-name
+  - ...
 model-index:
   - name: Bias Detector
     results:
       - task:
           type: text-classification
         dataset:
-          name: Your Dataset Name
-          type: dataset-type
+          name: ...
+          type: ...
         metrics:
           - type: accuracy
-            value: 0.92
+            value: ...
 ---
 
 # Bias Detector
@@ -29,10 +29,23 @@ This model is fine-tuned using **PEFT LoRA** on existing **Hugging Face models**
 - **Architecture:** Transformer-based (e.g., BERT, RoBERTa)
 - **Fine-tuning Method:** Parameter Efficient Fine-Tuning (LoRA)
 - **Use Case:** Bias classification, text summarization, sentiment analysis
-- **Dataset:** [Your Dataset Name](https://huggingface.co/datasets/your-dataset)
+- **Dataset:** [...](https://huggingface.co/datasets/your-dataset)
 - **Training Framework:** PyTorch + Transformers
 
 ## Usage
 To use this model, install the necessary libraries:
 ```bash
-pip install transformers torch
+pip install transformers torch
+```
+Then load the model with:
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model_name = "mjwagerman/bias-detector"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+text = "This is an example news headline."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+```