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@@ -1,89 +1,60 @@
----
-language: en
-license: apache-2.0
-tags:
-- text-classification
-- emotion
-- emotion-detection
-- affective-computing
-- distilbert
-- transformers
-- pytorch
-pipeline_tag: text-classification
-widget:
-- text: "I was skeptical at first, but the results completely blew me away with how good it turned out!"
-  example_title: "Joy Example"
-- text: "I have been sitting here in complete darkness, feeling utterly exhausted and defeated."
-  example_title: "Sadness Example"
-- text: "Get out of my face right now before I lose my temper completely!"
-  example_title: "Anger Example"
-- text: "Every single moment spent with you makes my life feel infinitely more meaningful."
-  example_title: "Love Example"
----
-
-# 🧠 DistilBERT Multiclass Emotion Classifier
-
-## 📌 Model Description
-This model is a fine-tuned **DistilBERT (Distilled Bidirectional Encoder Representations from Transformers)** sequence classification model trained to detect nuanced affective and emotional states from unstructured text.
-
-The model classifies text into 6 core emotional categories:
-* **Joy** 🌟
-* **Sadness** 🌧️
-* **Anger** ⚡
-* **Fear** 🛡️
-* **Love** ❤️
-* **Surprise** 🎁
-
----
-
-## 🚀 Quick Start with Python & Hugging Face `transformers`
-
-You can use this model directly with the Hugging Face `pipeline`:
-
-```python
-from transformers import pipeline
-
-# Initialize emotion pipeline
-classifier = pipeline("text-classification", model="Jayan12321/distilbert-emotion-classifier", top_k=None)
-
-# Run inference
-result = classifier("I was skeptical at first, but the results completely blew me away!")
-print(result)
-```
-
----
-
-## 🔬 Benchmark Performance (Held-Out Test Set)
-* **Accuracy:** `92.50%`
-* **Macro F1-Score:** `0.8755`
-* **Weighted F1-Score:** `0.9248`
-
-| Emotion | Precision | Recall | F1-Score | Support |
-| :--- | :---: | :---: | :---: | :---: |
-| **Joy** | 0.95 | 0.94 | 0.95 | 695 |
-| **Sadness** | 0.96 | 0.97 | 0.96 | 581 |
-| **Anger** | 0.92 | 0.91 | 0.92 | 275 |
-| **Fear** | 0.88 | 0.92 | 0.90 | 224 |
-| **Love** | 0.79 | 0.87 | 0.83 | 159 |
-| **Surprise** | 0.93 | 0.56 | 0.70 | 66 |
-
----
-
-## 🏭 Production Architecture Blueprint
-```
-        [ Free-Form Input Text ]
-                   │
-                   ▼  (<15ms SLA)
-     [ Fast Tokenizer (WordPiece) ]
-                   │
-                   ▼
- [ DistilBERT Sequence Classifier ]
-                   │
-    ┌──────────────┴──────────────┐
-    ▼                             ▼
-[ Argmax Class ]        [ Softmax Distribution ]
-• Primary Emotion         • Multi-class confidence
-```
-
----
-*Authored and published by [Jayan Gupta](https://huggingface.co/Jayan12321) • Data Scientist & AI Engineer*
+---
+language: en
+license: apache-2.0
+tags:
+- text-classification
+- emotion
+- emotion-detection
+- affective-computing
+- distilbert
+- transformers
+- pytorch
+pipeline_tag: text-classification
+widget:
+- text: "I was skeptical at first, but the results completely blew me away with how good it turned out!"
+  example_title: "Joy Example"
+- text: "I have been sitting here in complete darkness, feeling utterly exhausted and defeated."
+  example_title: "Sadness Example"
+- text: "Get out of my face right now before I lose my temper completely!"
+  example_title: "Anger Example"
+- text: "Every single moment spent with you makes my life feel infinitely more meaningful."
+  example_title: "Love Example"
+---
+
+# DistilBERT Multiclass Emotion Classifier 🎭
+
+A fine-tuned **DistilBERT (Distilled Bidirectional Encoder Representations from Transformers)** sequence classification model trained to detect nuanced affective and emotional states from unstructured natural language.
+
+- **Author**: [Jayan Gupta](https://huggingface.co/Jayan12321)
+
+---
+
+## 🎯 Model Overview & Classes
+The model classifies text across 6 primary emotional dimensions:
+* `0`: **Sadness** (grief, sorrow, exhaustion)
+* `1`: **Joy** (happiness, triumph, gratitude)
+* `2`: **Love** (affection, admiration, intimacy)
+* `3`: **Anger** (irritation, frustration, rage)
+* `4`: **Fear** (anxiety, dread, panic)
+* `5`: **Surprise** (shock, amazement, disbelief)
+
+---
+
+## 💻 Python Quickstart with Transformers Pipeline
+
+```python
+from transformers import pipeline
+
+# Load fine-tuned pipeline directly from Hugging Face
+classifier = pipeline(
+    "text-classification",
+    model="Jayan12321/distilbert-emotion-classifier"
+)
+
+# Run inference
+sample_text = "I received the scholarship offer this morning and I cannot stop smiling!"
+prediction = classifier(sample_text)
+
+print(prediction)
+# Output: [{'label': 'joy', 'score': 0.987}]
+```