Update README.md

README.md

@@ -10,7 +10,7 @@ language:
 ---
 #  BERT Base Intent model
 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.
-Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
+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.
@@ -35,23 +35,17 @@ The following hyperparameters were used during training:
 
 ### Model Description
 
-BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it
-was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of
-publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
-was pretrained with two objectives:
-
-- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
-  the entire masked sentence through the model and has to predict the masked words. This is different from traditional
-  recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
-  GPT which internally masks the future tokens. It allows the model to learn a bidirectional representation of the
-  sentence.
-- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
-  they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
-  predict if the two sentences were following each other or not.
-
-This way, the model learns an inner representation of the English language that can then be used to extract features
-useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard
-classifier using the features produced by the BERT model as inputs.
+The finetuned Hugging Face model is a variant of the BERT-base-uncased architecture, trained for intent classification 
+with three labels: fintech, abusive, and out of scope. The model has undergone a fine-tuning process, where it has been
+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 
+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 
+with three labels: fintech, abusive, and out of scope.
+
 
 - **Developed by:** Jeswin MS, Venkatesh R, Kushal S Ballari
 - **Model type:** Intent Classification