Update README.md

README.md

@@ -54,13 +54,12 @@ print(f"Predicted Event: {predicted_event}")
 The model focuses on structured and semi-structured log data, outputing around 60 different event categories. It is highly effective 
 for real-time log analysis, anomaly detection, and operational monitoring, helping organizations manage 
 large-scale network data by automatically classifying logs into predefined categories, facilitating faster 
-and more accurate diagnosis of network issues. The log-classifier-BERT-v1 model is designed to process logs as 
-input and output a corresponding classification.
+and more accurate diagnosis of network issues.
 
 ## Intended uses
 
-Our model is intended to be used as classifier. Given an input text (a log coming from a network/device), it outputs the corresponding event most associated with the log.
-The possible events that can be classified are shown in [encoder.json](https://huggingface.co/rahulm-selector/log-classifier-BERT-v1/blob/main/encoder-main.json)
+Our model is intended to be used as classifier. Given an input text (a log coming from a network/device/router), it outputs a corresponding event most associated with the log.
+The possible events that can be classified are shown in [encoder-main.json](https://huggingface.co/rahulm-selector/log-classifier-BERT-v1/blob/main/encoder-main.json)
 
 
 ## Training Details