Update README.md

README.md

@@ -10,17 +10,16 @@ datasets:
 
 This is an evolution of https://huggingface.co/aehrc/cxrmate developed for the Radiology Report Generation task of BioNLP @ ACL 2024.
 
-For this, proposed EAST: Entropy-Augmented Self-critical sequence Training (EAST). 
-EAST modifies Self-Critical Sequence Training (SCST) by adding entropy regularisation. 
-This helps maintain a higher entropy in the token distribution, 
-preventing overfitting to common phrases and ensuring a broader exploration of the vocabulary during training, 
-which is essential for handling the diversity of the radiology reports in the RRG24 datasets. 
-We apply this to a multimodal language model with RadGraph as the reward.
- 
-Additionally, our model incorporates several other aspects. 
-We use token type embeddings to differentiate between findings and impression section tokens, as well as image embeddings. 
-To handle missing sections, we employ special tokens. 
-We also utilise an attention mask with non-causal masking for the image embeddings and a causal mask for the report token embeddings.
+For this, we proposed EAST: Entropy-Augmented Self-critical sequence Training (EAST):
+ - EAST modifies Self-Critical Sequence Training (SCST) by adding entropy regularisation. 
+ - Helps maintain a higher entropy in the token distribution.
+ - Preventing overfitting to common phrases and ensuring a broader exploration of the vocabulary during training.
+ - This was essential to handle the diversity of the radiology reports in the RRG24 datasets. 
+
+EAST was applied to a multimodal language model with RadGraph as the reward. Other features include:
+ - Token type embeddings to differentiate between findings and impression section tokens, as well as image embeddings. 
+ - Special tokens (`NF` and `NI`) to handle missing *findings* and *impression* sections. 
+ - Non-causal attention masking for the image embeddings and a causal attention masking for the report token embeddings.
 
 ## How to use:
 
@@ -55,6 +54,9 @@ output_ids = model.generate(
 findings, impression = model.split_and_decode_sections(output_ids, tokenizer)
 ```
 
+## Notebook example:
+https://huggingface.co/aehrc/cxrmate-rrg24/blob/main/demo.ipynb
+
 ## Paper:
 
 ## Citation: