Update README.md

README.md

@@ -14,15 +14,21 @@ Does Not Require Visual Processing (0): The sentence is self-contained and can b
 
 The model is fine-tuned for sequence classification tasks and provides a straightforward interface to make predictions.
 
+# Fine-Tuning Information
+This model is fine-tuned based on the mDeBERTa-v3-base-mnli-xn model, which is a multilingual version of DeBERTa (Decoding-enhanced BERT with disentangled attention). The fine-tuning data used is primarily in Traditional Chinese, which makes the model well-suited for processing texts in this language. However, the model has been tested and can also perform well with English inputs.
+
+Base Model: [mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli)
+Fine-Tuning Data: Traditional Chinese text data
+
 # 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
 
@@ -62,4 +68,12 @@ if __name__ == "__main__":
         print(f"Text: {text}")
         print(f"Prediction: {prediction}\n")
 
-`
+```
+
+# Example Output
+
+For the input text "Hello, how are you?", the model might output:
+```
+Text: Hello, how are you?
+Prediction: No need for visual processing
+```