Update README.md

README.md

@@ -12,9 +12,7 @@ language:
 This is a fine tuned model based on Bert-Base-Uncased model. This model is used to classify intent into 3 categories- Fintech, Out of Scope and Abusive.
 The base line model is a pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
 [this paper](https://arxiv.org/abs/1810.04805) and first released in
-[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
-between english and English.
-
+[this repository](https://github.com/google-research/bert).
 ## Training procedure
 
 ### Training hyperparameters
@@ -40,7 +38,7 @@ with three labels: fintech, abusive, and out of scope. The model has undergone a
 trained on a large corpus of annotated data using a supervised learning approach. The objective of the model is to 
 classify incoming text data into one of the three predefined classes based on the underlying intent of the text.
 
-The performance of the model was evaluated on a held-out test set, and it achieved high accuracy and F1 scores 
+The performance of the model was evaluated and it achieved high accuracy and F1 scores 
 for all three classes. The model's high accuracy and robustness make it suitable for use in real-world applications, 
 such as chatbots, customer service automation, and social media monitoring.
 Overall, the finetuned Hugging Face model provides an effective and reliable solution for intent classification