Update README.md

README.md

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 <!-- Provide a quick summary of what the model is/does. -->
 
-This model is designed to categorize text into two classes: "Safe", or "Nsfw", which makes it suitable for content moderation and filtering applications.
+This model is designed to categorize text into two classes: "safe", or "nsfw", which makes it suitable for content moderation and filtering applications.
 
-The model was trained using a dataset containing 190,000 labeled text samples, distributed among the two classes of "Safe" and "Nsfw".
+The model was trained using a dataset containing 190,000 labeled text samples, distributed among the two classes of "safe" and "nsfw".
 
 The model is based on the Distilbert-base model. 
 
-In terms of performance, the model has achieved a score of 0.95 for F1 and 0.95 for accuracy. 
+In terms of performance, the model has achieved a score of 0.988 for F1.
 ### Model Description
 
 The model can be used directly to classify text into one of the two classes. It takes in a string of text as input and outputs a probability distribution over the two classes. The class with the highest probability is selected as the predicted class.
@@ -49,18 +49,20 @@ The model can be used directly to classify text into one of the two classes. It
 
 ### Training Procedure
 
-The model was trained utilizing the Hugging Face Transformers library. The training approach employed transfer learning, where the original layers of the Distilbert-base model were frozen, and only the classification layers were fine-tuned on the labeled dataset. This selective fine-tuning allowed the model to leverage the pre-existing knowledge of the Distilbert-base model while adapting to the specific task at hand. To optimize memory usage and accelerate training, mixed precision fp16 was used. Further details regarding the training procedure can be found in the Technical Specifications section.
+The model was trained utilizing the Hugging Face Transformers library. The training approach involved first fine-tuning the DistilBERT-base model and then applying transfer learning. Initially, the entire DistilBERT-base model was fine-tuned on the labeled dataset. Following this, transfer learning was employed by freezing the original layers of the fine-tuned DistilBERT model and fine-tuning only the classification layers. This approach allowed the model to leverage the fine-tuned knowledge of the DistilBERT-base model while adapting further to the specific task at hand. To optimize memory usage and accelerate training, mixed precision FP16 was used.
 
 ### Training Data
 
-The training data for the text classification model consists of a large corpus of text labeled with one of the two classes: "Safe" and "Nsfw". The dataset contains a total of 190,000 examples, which are distributed as follows:
+The training data for finetuning the text classification model consists of a large corpus of text labeled with one of the two classes: "safe" and "nsfw". The dataset contains a total of 190,000 examples, which are distributed as follows:
 
-100,000 examples labeled as "Safe"
+100,000 examples labeled as "safe"
 
-90,000 examples labeled as "Nsfw"
+90,000 examples labeled as "nsfw"
 
 The data was preprocessed to remove stop words and punctuation, and to convert all text to lowercase.
 
+After fine-tuning the DistilBERT-base model on this dataset, transfer learning was applied using a smaller dataset. For transfer learning, the original layers of the fine-tuned DistilBERT model were frozen, and only the classification layers were fine-tuned on an additional dataset containing 40,000 examples.
+
 More information about the training data can be found in the Dataset Card (availabe soon).
 
 ## Uses