readme: ---
  tags:
  - bert
  ---
  
  # Model Card for biosyn-sapbert-ncbi-disease
   
   
   
  # Model Details
   
  ## Model Description
   
  More information needed
   
  - **Developed by:** Dmis-lab (Data Mining and Information Systems Lab, Korea University)
  - **Shared by [Optional]:** Hugging Face
  - **Model type:** Feature Extraction
  - **Language(s) (NLP):** More information needed
  - **License:** More information needed
  - **Related Models:** 
    - **Parent Model:** BERT
  - **Resources for more information:** 
    - [GitHub Repo](https://github.com/jhyuklee/biobert)
    - [Associated Paper](https://arxiv.org/abs/1901.08746)
   
  # Uses
   
   
  ## Direct Use
   
  This model can be used for the task of Feature Extraction
   
  ## Downstream Use [Optional]
   
  More information needed
   
  ## Out-of-Scope Use
   
  The model should not be used to intentionally create hostile or alienating environments for people. 
   
  # Bias, Risks, and Limitations
   
  Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
   
   
  ## Recommendations
   
  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
   
   
  # Training Details
   
  ## Training Data
  The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf)
  > We used the BERTBASE model pre-trained on English Wikipedia and BooksCorpus for 1M steps. BioBERT v1.0 (þ PubMed þ PMC) is the version of BioBERT (þ PubMed þ PMC) trained for 470 K steps. When using both the PubMed and PMC corpora, we found that 200K and 270K pre-training steps were optimal for PubMed and PMC, respectively. We also used the ablated versions of BioBERT v1.0, which were pre-trained on only PubMed for 200K steps (BioBERT v1.0 (þ PubMed)) and PMC for 270K steps (BioBERT v1.0 (þ PMC))
   
  ## Training Procedure
   
  ### Preprocessing
   The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf)
  > We pre-trained BioBERT using Naver Smart Machine Learning (NSML) (Sung et al., 2017), which is utilized for large-scale experiments that need to be run on several GPUs
   
  ### Speeds, Sizes, Times
   The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf)
  >  The maximum sequence length was fixed to 512 and the mini-batch size was set to 192, resulting in 98 304 words per iteration. 
   
  # Evaluation
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
  More information needed
   
  ### Factors
   
  More information needed
   
  ### Metrics
   
   
   
  More information needed
   
  ## Results 
  More information needed
   
  # Model Examination
   
  More information needed
   
  # Environmental Impact
   
  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
   
  - **Hardware Type:** 
  - **Training:** Eight NVIDIA V100 (32GB) GPUs [ for training],  
  - **Fine-tuning:**  a single NVIDIA Titan Xp (12GB) GPU to fine-tune BioBERT on each task
  - **Hours used:** More information needed
  - **Cloud Provider:** More information needed
  - **Compute Region:** More information needed
  - **Carbon Emitted:** More information needed
   
  # Technical Specifications [optional]
   
  ## Model Architecture and Objective
   
  More information needed
   
  ## Compute Infrastructure
   
  More information needed
   
  ### Hardware
   
  More information needed
   
  ### Software
   
  More information needed
   
  # Citation
   
   
  **BibTeX:**
   ```
  @article{lee2019biobert,
    title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining},
    author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
    journal={arXiv preprint arXiv:1901.08746},
    year={2019}
  }
  ```
    
  # Glossary [optional]
   
  More information needed
   
  # More Information [optional]
   
  For help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee(`lee.jnhk (at) gmail.com`), or Wonjin Yoon (`wonjin.info (at) gmail.com`) for communication related to BioBERT.
   
  # Model Card Authors [optional]
   
   
   Dmis-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team
   
  # Model Card Contact
   
  More information needed
   
  # How to Get Started with the Model
   
  Use the code below to get started with the model.
   
  <details>
  <summary> Click to expand </summary>
  
  ```python
  from transformers import AutoTokenizer, AutoModel
   
  tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biosyn-sapbert-ncbi-disease")
   
  model = AutoModel.from_pretrained("dmis-lab/biosyn-sapbert-ncbi-disease")
   
  `