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 ---
 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)  |
-| BC5CDR-chemical |  Named-entity recognition |  93.7 (0.2)  |
-| ShARe/CLEFE     |  Named-entity recognition |  80.1 (0.4)  |
-| DDI             |  Relation extraction      |  81.1 (1.5)  |
-| ChemProt        |  Relation extraction      |  75.0 (0.3)  |
-| 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)**|
+
+
+| 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)  |
+| BC5CDR-chemical |  Named-entity recognition    |  93.7 (0.2)  |
+| ShARe/CLEFE     |  Named-entity recognition    |  80.1 (0.4)  |
+| DDI             |  Relation extraction         |  81.1 (1.5)  |
+| ChemProt        |  Relation extraction         |  75.0 (0.3)  |
+| i2b2 2010       |  Relation extraction         |  74.0 (0.8)  |
+| HoC             |  Document classification     |  86.4 (0.5)  |
+| MedNLI          |  Inference                   |  83.6 (0.7)  |
+| **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.">