Update README.md

README.md

@@ -1,26 +1,62 @@
-
 ---
 tags:
 - autotrain
 - text-classification
 base_model: sentence-transformers/all-mpnet-base-v2
 widget:
-- text: "I love AutoTrain"
+- text: I love AutoTrain
+language:
+- en
+pipeline_tag: text-classification
+---
+
+# Clickbait Detection Model
+
+This is a **custom-trained text classification model** created using Hugging Face **AutoTrain**. The model is designed to classify text into two categories:
+- **Clickbait**
+- **Not Clickbait**
+
+The training was conducted using a fine-tuned version of the `sentence-transformers/all-mpnet-base-v2` base model, which is well-suited for text classification tasks.
+
 ---
 
-# Model Trained Using AutoTrain
+## Model Details
+
+- **Base Model**: [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
+- **Problem Type**: Text Classification
+- **Language**: English (`en`)
+- **Pipeline Tag**: text-classification
+- **Tags**: autotrain, text-classification
+
+---
+
+## Usage
+
+You can use this model with Hugging Face’s `transformers` library to classify text into `clickbait` or `not clickbait`.
 
-- Problem type: Text Classification
+### Example Code
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-## Validation Metrics
-loss: 0.0028607482090592384
+# Load tokenizer and model
+model_name = "Milan97/autotrain-9ikup-ih7yd"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-f1: 1.0
+# Input text
+text = "You won’t believe what happened next!"
 
-precision: 1.0
+# Tokenize and perform inference
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+outputs = model(**inputs)
 
-recall: 1.0
+# Get predicted label and confidence
+logits = outputs.logits
+predicted_class = logits.argmax(dim=1).item()
+confidence = logits.softmax(dim=1).max().item()
 
-auc: 1.0
+# Label mapping
+labels = {0: "Not Clickbait", 1: "Clickbait"}
 
-accuracy: 1.0
+print(f"Text: {text}")
+print(f"Prediction: {labels[predicted_class]} (Confidence: {confidence:.2f})")