Update README.md

README.md

@@ -36,19 +36,16 @@ DeviceBERT utilizes BioBERT (dmis-lab/biobert-base-cased-v1.2) which has been tr
 The model was trained utilizing a processed and annotated NER dataset created using the OpenFDA Device Recalls Dataset (https://open.fda.gov/apis/device/recall/), and further
 tokenized using the DistilBERT AutoTokenizer. It can be used to perform inferencing to accurately identfiy and label medical device, device component, part number and trade name related terms in a variety of downstream applications. 
 
-Paper: "DeviceBERT: Applied Transfer Learning With Targeted Annotations and Vocabulary Enrichment to Identify Medical Device and Component Terminology in FDA Recall Summaries"
-
 - **Developed by:** Miriam Farrington for CS224N
 - **Model type:** Deep Learning Language Model/LLM
 - **Language(s) (NLP):** Python, TensorFlow
-- **Github:** https://github.com/mmfarrington/devicebert-ner-project
 - **License:** MIT
 - **Finetuned from model [optional]:** BioBERT (dmis-lab/biobert-base-cased-v1.2)
 
 ### Model Sources [optional]
 
-- **Repository:** Link TBD
-- **Paper [optional]:** Link TBD
+- **Repository:** https://github.com/mmfarrington/devicebert-ner-project
+- **Paper:** "DeviceBERT: Applied Transfer Learning With Targeted Annotations and Vocabulary Enrichment to Identify Medical Device and Component Terminology in FDA Recall Summaries"
 
 ## Uses