Update README.md

README.md

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 base_model:
 - airesearch/wangchanberta-base-att-spm-uncased
 ---
-
 # AmbatronBERTa
 
-AmbatronBERTa is a Thai language model fine-tuned for text classification tasks.
+AmbatronBERTa is a Thai language model fine-tuned specifically for text classification tasks, built upon the WangchanBERTa architecture.
 
 ## Model Description
-AmbatronBERTa is based on the CamemBERT architecture, designed to handle Thai text. It is trained on a large, diverse corpus of Thai data.
+AmbatronBERTa is designed to handle the complexities of the Thai language. It has been fine-tuned on a dataset of over 3,000 research papers to improve classification accuracy. Leveraging the transformer-based WangchanBERTa, it efficiently captures the nuances of Thai text, making it suitable for classifying documents across multiple fields.
+
+## Developers
+AmbatronBERTa was developed by students at **King Mongkut's University of Technology North Bangkok**:
+- **Peerawat Banpahan**
+- **Waris Thongpho**
+
+## Use Cases
+AmbatronBERTa can be applied to a wide range of tasks, such as:
+- **Research Classification:** Categorizing academic papers into relevant topics.
+- **Document Organization:** Classifying articles, blogs, and other documents by themes.
+- **Sentiment Analysis:** Analyzing sentiment in Thai-language texts across various contexts.
 
 ## How to Use
-To use this model in your project, you can use the `transformers` library:
+To use AmbatronBERTa with the `transformers` library:
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
+# Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("Peerawat2024/AmbatronBERTa")
 model = AutoModelForSequenceClassification.from_pretrained("Peerawat2024/AmbatronBERTa")