Biosyn Sapbert Ncbi Disease

🚀 biosyn-sapbert-ncbi-disease

A model for feature extraction in the biomedical domain, based on the BERT architecture.

🚀 Quick Start

Use the code below to get started with the model.

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")

✨ Features

This model can be used for the task of Feature Extraction.

📚 Documentation

📋 Model Details

• Developed by: Dmis-lab (Data Mining and Information Systems Lab, Korea University)
• Shared by [Optional]: Hugging Face
• Model type: Feature Extraction
• Related Models:
  • Parent Model: BERT
• Resources for more information:
  • GitHub Repo
  • Associated Paper

📖 Uses

Direct Use

This model can be used for the task of Feature Extraction.

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

More information needed for testing data, factors, metrics, and results.

🌱 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

📜 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}
}

📞 More Information

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

Dmis-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team