Update README.md (model_card) (#4424)

- add a citation.
- modify the table of the BLUE benchmark.

The table of the first version was not displayed correctly on https://huggingface.co/seiya/oubiobert-base-uncased.
Could you please confirm that this fix will allow you to display it correctly?

model_cards/seiya/oubiobert-base-uncased/README.md

@@ -1,18 +1,22 @@
 ---
 tags:
-- pytorch
 - exbert
 license: apache-2.0
 ---
+
 # ouBioBERT-Base, Uncased
+
 Bidirectional Encoder Representations from Transformers for Biomedical Text Mining by Osaka University (ouBioBERT) is a language model based on the BERT-Base (Devlin, et al., 2019) architecture. We pre-trained ouBioBERT on PubMed abstracts from the PubMed baseline (ftp://ftp.ncbi.nlm.nih.gov/pubmed/baseline) via our method.  
 
 The details of the pre-training procedure can be found in Wada, et al. (2020).  
 
 ## Evaluation
+
 We evaluated the performance of ouBioBERT in terms of the biomedical language understanding evaluation (BLUE) benchmark (Peng, et al., 2019). The numbers are mean (standard deviation) on five different random seeds.  
+
+
 | Dataset         |  Task Type                   |  Score       |
-|:----------------|:--------------------------|-------------:|
+|:----------------|:-----------------------------|-------------:|
 | MedSTS          |  Sentence similarity         |  84.9 (0.6)  |
 | BIOSSES         |  Sentence similarity         |  92.3 (0.8)  |
 | BC5CDR-disease  |  Named-entity recognition    |  87.4 (0.1)  |
@@ -23,15 +27,23 @@ We evaluated the performance of ouBioBERT in terms of the biomedical language un
 | i2b2 2010       |  Relation extraction         |  74.0 (0.8)  |
 | HoC             |  Document classification     |  86.4 (0.5)  |
 | MedNLI          |  Inference                   |  83.6 (0.7)  |
-| **Total**       |  -                        |**83.8 (0.3)**|
+| **Total**       |  Macro average of the scores |**83.8 (0.3)**|
+
 
 ## Code for Fine-tuning
 We made the source code for fine-tuning freely available at [our repository](https://github.com/sy-wada/blue_benchmark_with_transformers).
 
 ## Citation
+
 If you use our work in your research, please kindly cite the following paper:  
+
 ```bibtex
-now preparing...
+@misc{2005.07202,
+Author = {Shoya Wada and Toshihiro Takeda and Shiro Manabe and Shozo Konishi and Jun Kamohara and Yasushi Matsumura},
+Title = {A pre-training technique to localize medical BERT and enhance BioBERT},
+Year = {2020},
+Eprint = {arXiv:2005.07202},
+}
 ```
 
 <a href="https://huggingface.co/exbert/?model=seiya/oubiobert-base-uncased&sentence=Coronavirus%20disease%20(COVID-19)%20is%20caused%20by%20SARS-COV2%20and%20represents%20the%20causative%20agent%20of%20a%20potentially%20fatal%20disease%20that%20is%20of%20great%20global%20public%20health%20concern.">