Update README.md

README.md

@@ -1,19 +1,36 @@
+%%writefile README.md
 ---
+language: en
+tags:
+- text-classification
+- emotions
+- sentiment-analysis
 datasets:
 - AiLab-IMCS-UL/twitter_emotions-en
-language:
-- en
-base_model:
-- distilbert/distilbert-base-uncased
-pipeline_tag: text-classification
+widget:
+- text: "I'm so happy today!"
+- text: "I feel really sad and lonely."
+- text: "This makes me so angry!"
+---
 
-id2label:
-  '0': 'sadness'
-  '1': 'joy'
-  '2': 'love'
-  '3': 'anger'
-  '4': 'fear'
-  '5': 'surprise'
+# Emotion Classification Model
 
----
+This model classifies text into 6 emotions: sadness, joy, love, anger, fear, surprise.
+
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("your-username/emotion-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("your-username/emotion-classifier")
+
+text = "I'm so happy!"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+predicted_class = torch.argmax(predictions).item()
 
+emotions = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']
+print(f"Emotion: {emotions[predicted_class]}")
+```