Update README.md

README.md

@@ -14,10 +14,9 @@ datasets:
 - vazish/autofill_dataset
 ---
 
-## TinyBert: a Compact Task-Agnostic BERT for Resource-Limited Devices
+## BERT Miniatures
 
-Tiny is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
-between self-attentions and feed-forward networks.
+This is the tiny version of the 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking).
 
 This checkpoint is the original TinyBert Optimized Uncased English:
 [TinyBert](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2)
@@ -83,4 +82,13 @@ CC Expiration Month      0.972     0.972     0.972        36
            accuracy                          0.967      1846
           macro avg      0.923     0.907     0.910      1846
        weighted avg      0.968     0.967     0.967      1846
+```
+
+```
+@article{turc2019,
+  title={Well-Read Students Learn Better: On the Importance of Pre-training Compact Models},
+  author={Turc, Iulia and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
+  journal={arXiv preprint arXiv:1908.08962v2 },
+  year={2019}
+}
 ```