Update README.md

README.md

@@ -1,6 +1,6 @@
-# 🎯 Tone Detection using `facebook/bart-large-mnli` (Zero-Shot Classification)
+# 🎯 Tone Detection using `yiyanghkust/finbert-tone`
 
-This project demonstrates how to perform **Tone Detection** using the [`facebook/bart-large-mnli`](https://huggingface.co/facebook/bart-large-mnli) model through **zero-shot classification** based on Natural Language Inference (NLI).
+This project demonstrates how to perform **Tone Detection** using the [`yiyanghkust/finbert-tone`].
 
 This approach enables you to classify emotional tone (e.g., Positive, Negative, Neutral, etc.) **without training**, by framing it as a textual entailment task.
 
@@ -8,9 +8,9 @@ This approach enables you to classify emotional tone (e.g., Positive, Negative,
 
 ## 📌 Model Details
 
-- **Model:** `facebook/bart-large-mnli`
-- **Task:** Zero-shot classification via NLI
-- **Approach:** Checks if the input sentence entails a hypothesis (e.g., "This text expresses anger.")
+- **Model:** `yiyanghkust/finbert-tone`
+- **Task:** Detect the tone of financial text
+- **Approach:** Checks if the input sentence entails a hypothesis (e.g., "Positive" or Negative)
 - **Strength:** No labeled training data required
 
 ---
@@ -29,7 +29,7 @@ dataset = load_dataset("go_emotions")
 ```Python
 from transformers import pipeline
 
-classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+classifier = pipeline("zero-shot-classification", model="yiyanghkust/finbert-tone")
 
 labels = ["positive", "neutral", "negative"]