update readme

README.md

@@ -2,27 +2,26 @@
 
 ## Model Details
 The banT5 model is a Bangla adaptation of the T5 (Text-To-Text Transfer Transformer) model, originally introduced by researchers at Google. T5 is a unified language model designed to frame all natural language processing (NLP) tasks as text-to-text problems. This allows the model to handle a variety of tasks by simply altering the input and output formats.
+
 banT5 is specifically trained on a curated Bangla text corpus to deliver state-of-the-art performance in tasks like `Named Entity Recognition (NER), Part-of-Speech (POS) tagging, Question Answering,Paraphrase Identification,etc.`
 ## Training Data
-The banT5 model was pre-trained on a large-scale Bangla text dataset, amounting to 27 GB of raw data. The corpus was carefully processed to produce a final dataset of 36 GB after cleaning and normalization.
-### Data Information
-| Metric              | Count                |
-|---------------------|----------------------|
-| Total words         | 1,646,252,743 (1.65 billion) |
-| Unique words        | 15,223,848 (15.23 million) |
-| Total sentences     | 131,412,177 (131.4 million) |
-| Total documents     | 7,670,661 (7.67 million) |
+The banT5 model was pre-trained on a large-scale Bangla text dataset, amounting to **27 GB** of raw data. After cleaning and normalization, the processed dataset increased to **36 GB**. Below is an overview of the data cardinalities:
+| **Metric**           | **Count**                       |
+|-----------------------|---------------------------------|
+| **Total words**       | 1,646,252,743 (1.65 billion)   |
+| **Unique words**      | 15,223,848 (15.23 million)     |
+| **Total sentences**   | 131,412,177 (131.4 million)    |
+| **Total documents**   | 7,670,661 (7.67 million)       |
 ## Results
-The banT5 model demonstrated strong performance on downstream tasks. Below is the summary of results for NER and POS tagging:
-| Task                | Metric       | Value    |
-|---------------------|--------------|----------|
-| Named Entity Recognition (NER) | Precision    | 0.8882   |
-|                     | Recall       | 0.8563   |
-|                     | Macro F1     | 0.8686   |
-| Part-of-Speech (POS) Tagging | Precision    | 0.8813   |
-|                     | Recall       | 0.8813   |
-|                     | Macro F1     | 0.8791   |
-
+The banT5 model demonstrated strong performance on downstream tasks, as summarized below:
+| **Task**                 | **Metric**   | **Value**  |
+|--------------------------|--------------|------------|
+| **Named Entity Recognition (NER)** | Precision  | 0.8882     |
+|                          | Recall      | 0.8563     |
+|                          | Macro F1    | 0.8686     |
+| **Part-of-Speech (POS) Tagging**    | Precision  | 0.8813     |
+|                          | Recall      | 0.8813     |
+|                          | Macro F1    | 0.8791     |
 ## Using this model in `transformers`
 
 ```bash