Update README.md

README.md

@@ -1,36 +1,7 @@
-%%writefile README.md
 ---
-language: en
-tags:
-- text-classification
-- emotions
-- sentiment-analysis
 datasets:
 - AiLab-IMCS-UL/twitter_emotions-en
-widget:
-- text: "I'm so happy today!"
-- text: "I feel really sad and lonely."
-- text: "This makes me so angry!"
----
-
-# 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]}")
-```
+base_model:
+- distilbert/distilbert-base-uncased
+pipeline_tag: text-classification
+---