Update README.md

README.md

@@ -25,6 +25,47 @@ Alongside these models, FinBERT, different versions of RoBERTa, and EconBERT wer
 ## How to use
 
 ```python
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# Load model and tokenizer
+model_name = "brjoey/CBSI-bert-base-uncased"
+classifier = pipeline(
+    "text-classification",
+    model=model_name,
+    tokenizer=model_name
+)
+
+# Define label mapping
+cbsi_label_map = {
+    0: "neutral",
+    1: "dovish",
+    2: "hawkish"
+}
+
+# Classify a column in a Pandas DataFrame - replace with your DataFrame
+texts = [
+    "The Governing Council decided to lower interest rates.",
+    "The central bank will maintain its current policy stance."
+]
+
+df = pd.DataFrame({
+    "text": texts
+})
+
+# Run classification 
+predictions = classifier(
+    df["text"].tolist()
+)
+
+# Store the results
+df["label"], df["score"] = zip(*[
+    (cbsi_label_map[int(pred["label"].split("_")[-1])], pred["score"])
+    for pred in predictions
+])
+
+print("\n                              === Results ===\n")
+print(df[["text", "label", "score"]])
 ```
 ---
 # Citation