Update README.md

README.md

@@ -16,5 +16,22 @@ The corpus consists of two components:
 2) an evaluation set, which comprises 100 scientific articles manually annotated for evaluation purposes.
 To the best of our knowledge, SciDMT is the largest corpus for scientific entity mention detection. The corpus’s scale and diversity are instrumental in developing and refining models for tasks such as indexing scientific papers, enhancing information retrieval, and improving the accessibility of scientific knowledge. We demonstrate the corpus’s utility through experiments with advanced deep learning architectures like SciBERT and GPT-3.5. Our findings establish performance baselines and highlight unresolved challenges in scientific mention detection. SciDMT serves as a robust benchmark for the research community, encouraging the development of innovative models to further the field of scientific information extraction.
 
+# Files
+
+    'DICT': '../data/SciDMT/SciDMT_dict.json',
+
+    # machine learning inputs at sentence level
+    'sent_xy': '../data/SciDMT/SciDMT_sentences.p', 
+    'sent_eval': '../data/SciDMT/SciDMT_E_sentences.json',
+    'sent_split': '../data/SciDMT/SciDMT_sentences_split.json',
+
+    # document level inputs
+    'doc_split': '../data/SciDMT/SciDMT_split.json',
+    'doc_eval': 'SciDMT_E_human_annotations.json',
+    'doc_text_and_meta': 'SciDMT_papers.csv',
+
+# Usage
+SciDMT_demo.ipynb describes how to open the files and print out the file structures. 
+
 # Citation
 To-be-post