updated readme

README.md

@@ -1,6 +1,8 @@
-The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). These BERT variants were introduced in the paper [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962). These models are supposed to be trained on a downstream task.
+The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). 
 
-If you use the model, please consider citing the paper
+This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini), [bert-tiny](https://huggingface.co/prajjwal1/bert-tiny), [bert-small](https://huggingface.co/prajjwal1/bert-small) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study [Well-Read Students Learn Better: On the Importance of Pre-training Compact Models](https://arxiv.org/abs/1908.08962), and ported to HF for the study [Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics](https://arxiv.org/abs/2110.01518). These models are supposed to be trained on a downstream task.
+
+If you use the model, please consider citing both the papers:
 ```
 @misc{bhargava2021generalization,
       title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, 
@@ -10,14 +12,47 @@ If you use the model, please consider citing the paper
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
+
+@article{DBLP:journals/corr/abs-1908-08962,
+  author    = {Iulia Turc and
+               Ming{-}Wei Chang and
+               Kenton Lee and
+               Kristina Toutanova},
+  title     = {Well-Read Students Learn Better: The Impact of Student Initialization
+               on Knowledge Distillation},
+  journal   = {CoRR},
+  volume    = {abs/1908.08962},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1908.08962},
+  eprinttype = {arXiv},
+  eprint    = {1908.08962},
+  timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
 ```
+
+Other models to check out:
+- `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny)
+- `prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini)
+- `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small)
+- `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium)
+
 Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli).
 
+Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)
+
 
-You can check out:
-- `prajjwal1/bert-tiny` (L=2, H=128)
-- `prajjwal1/bert-mini` (L=4, H=256)
-- `prajjwal1/bert-small` (L=4, H=512)
-- `prajjwal1/bert-medium` (L=8, H=512)
+---
+language: 
+  - en
+  
+tags:
+- BERT
+- MNLI
+- NLI
+- transformer
+- pre-training
 
-[@prajjwal_1](https://twitter.com/prajjwal_1)
+---