Update README.md

README.md

@@ -15,7 +15,7 @@ tags:
 
 <!-- Provide a quick summary of what the model is/does. -->
 
- I've developed an abstractive text summarization model using the T5 transformer architecture in PyTorch. I've fine-tuned the model on my specific dataset to create concise and coherent summaries of longer texts. This model takes advantage of transformer libraries and the power of multi-head self-attention to capture context and dependencies in the input text. It's a valuable tool for generating human-like summaries, making information extraction and condensation more efficient.
+ I've developed an abstractive text summarization model using the T5 transformer architecture in PyTorch. I've fine-tuned the model on cnn_dailymail dataset to create concise and coherent summaries of longer texts. This model takes advantage of transformer libraries and the power of multi-head self-attention to capture context and dependencies in the input text. It's a valuable tool for generating human-like summaries, making information extraction and condensation more efficient.
 ## To use my model
 
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer