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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:

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) and Bender et al. (2021)). 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

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

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

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 presented in Lacoste et al. (2019).

  • 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.

Click to expand
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")
 
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