Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+# Vision_or_not: A Multimodal Text Classification Model
+
+Vision_or_not is a text classification model designed to determine whether a given sentence requires visual processing or not. This model is part of a multimodal framework, enabling efficient analysis of text and its potential need for visual processing, useful in applications like visual question answering (VQA) and other AI systems that require understanding both textual and visual content.
+
+# Model Overview
+
+This model classifies sentences into two categories:
+
+Requires Visual Processing (1): The sentence contains content that necessitates additional visual information for full understanding.
+Does Not Require Visual Processing (0): The sentence is self-contained and can be processed without any visual input.
+
+The model is fine-tuned for sequence classification tasks and provides a straightforward interface to make predictions.
+
+# Quick Start
+
+To use the Vision_or_not model, you will need to install the following Python libraries:
+`
+pip install transformers torch
+`
+
+To use the model for making predictions, simply load the model and tokenizer, then pass your text to the prediction function. Below is an example code for usage:
+`
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Set up the device (GPU if available, otherwise CPU)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Label mapping dictionary
+label_mapping = {
+    0: "No need for visual processing",
+    1: "Requires visual processing",
+}
+
+def predict_emotion(text, model_path="Johnson8187/Vision_or_not"):
+    # Load model and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    model = AutoModelForSequenceClassification.from_pretrained(model_path).to(device)
+
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
+
+    # Perform the prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Get predicted class
+    predicted_class = torch.argmax(outputs.logits).item()
+    predicted_label = label_mapping[predicted_class]
+
+    return predicted_label
+
+if __name__ == "__main__":
+    # Example usage
+    test_texts = [
+        "Hello, how are you?",
+    ]
+
+    for text in test_texts:
+        prediction = predict_emotion(text)
+        print(f"Text: {text}")
+        print(f"Prediction: {prediction}\n")
+
+`