metadata

tags:
  - autotrain
  - text-classification
base_model: sentence-transformers/all-mpnet-base-v2
widget:
  - 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 Details

Base Model: 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.

Example Code

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load tokenizer and model
model_name = "Milan97/ClickbaitDetectionModel"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Input text
text = "You won’t believe what happened next!"

# Tokenize and perform inference
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)

# Get predicted label and confidence
logits = outputs.logits
predicted_class = logits.argmax(dim=1).item()
confidence = logits.softmax(dim=1).max().item()

# Label mapping
labels = {0: "Not Clickbait", 1: "Clickbait"}

print(f"Text: {text}")
print(f"Prediction: {labels[predicted_class]} (Confidence: {confidence:.2f})")