updated readme

README.md

@@ -51,13 +51,12 @@ This research project builds a machine learning model that can **automatically a
 ## 📊 Dataset Statistics
 
 The dataset used for training consists of real-world accessibility bug reports, each labeled with one of four priority levels. The distribution of labels is imbalanced, and label-aware preprocessing steps were taken to improve model performance.
-
 | Label | Priority Level | Count |
 |-------|----------------|-------|
-| 1     | Medium         | 2035  |
-| 2     | High           | 1465  |
-| 0     | Low            | 804   |
-| 3     | Critical       | 756   |
+| 1     | Critical       | 2035  |
+| 2     | Major          | 1465  |
+| 0     | Blocker        | 804   |
+| 3     | Minor          | 756   |
 
 **Total Samples**: 5,060
 
@@ -75,10 +74,10 @@ The dataset consists of short bug report texts labeled with one of four priority
 
 | Label | Meaning     |
 |-------|-------------|
-| 0     | Low         |
-| 1     | Medium      |
-| 2     | High        |
-| 3     | Critical    |
+| 0     | Blocker     |
+| 1     | Critical    |
+| 2     | Major       |
+| 3     | Minor       |
 
 ### ✏️ Sample Entries:
 
@@ -124,8 +123,8 @@ This model fine-tunes `roberta-base` using a 4-class custom dataset to classify
 from transformers import RobertaTokenizer, RobertaForSequenceClassification
 import torch
 
-model = RobertaForSequenceClassification.from_pretrained("your-username/roberta-priority-multiclass")
-tokenizer = RobertaTokenizer.from_pretrained("your-username/roberta-priority-multiclass")
+model = RobertaForSequenceClassification.from_pretrained("shivamjadhav/roberta-priority-multiclass")
+tokenizer = RobertaTokenizer.from_pretrained("shivamjadhav/roberta-priority-multiclass")
 
 inputs = tokenizer("VoiceOver skips over text with <strong> tags", return_tensors="pt")
 outputs = model(**inputs)