Update README.md

README.md

@@ -23,7 +23,7 @@ datasets:
 
 We trained the model for 2.4M steps (180 epochs) with the final perplexity over the development set being 3.97 (similar to English BERT-base).
 
-This IndoBERT was used to examine IndoLEM - an Indonesian benchmark that comprises of seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse.
+This IndoBERT was used to examine IndoLEM - an Indonesian benchmark that comprises of seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse.[[1]](#1)
 
 ## Details of the downstream task (Q&A) - Dataset
 SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
@@ -40,4 +40,7 @@ The model was trained on a Tesla T4 GPU and 12GB of RAM.
 | Metric | # Value   |
 | ------ | --------- |
 | **EM** | **51.61** |
-| **F1** | **69.09** |
+| **F1** | **69.09** |
+
+### Reference
+<a id="1">[1]</a>Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin. 2020. IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP. Proceedings of the 28th COLING.