Update README.md

README.md

@@ -6,4 +6,61 @@ base_model:
 - lyeonii/bert-tiny
 pipeline_tag: text-classification
 ---
-Fine-tuned model to predict whether a text contains wage information.
+
+# is_pay
+
+`is_pay` is a fine-tuned, lightweight sequence classification model used to predict whether a given text string contains wage or salary information. It was fine-tuned from `lyeonii/bert-tiny`, making it highly efficient for high-throughput filtering pipelines.
+
+## Basic Usage
+
+You can deploy this model using the standard Hugging Face `text-classification` pipeline.
+
+```python
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+
+model_name = "loyoladatamining/is_pay"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name, max_length=64, truncation=True)
+
+# Create text classification pipeline
+nlp = pipeline(
+    "text-classification", 
+    model=model, 
+    tokenizer=tokenizer,
+    max_length=64,
+    truncation=True
+)
+
+# Inference
+text = "The starting salary for this position is $75,000 per year."
+result = nlp(text)
+print(result)
+```
+
+## Output Format
+
+The model returns a list containing a dictionary with the predicted binary class label and its corresponding confidence score:
+
+```json
+[
+  {
+    "label": "LABEL_1",
+    "score": 0.9942
+  }
+]
+```
+
+### Label Mapping
+ - `LABEL_0`: The text does not contain wage or salary information.
+ - `LABEL_1`: The text contains wage or salary information.
+
+## Citation
+If you find `is_pay` useful in your work, please consider citing:
+
+```
+@article{meisenbacher2025extracting,
+  title={Extracting O* NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data},
+  author={Meisenbacher, Stephen and Nestorov, Svetlozar and Norlander, Peter},
+  year={2025}
+}
+```