tags:
- feature-extraction
- bert
Model Card for biosyn-sapbert-bc5cdr-disease
Model Details
Model Description
More information needed
- Developed by: DMIS-lab (Data Mining and Information Systems Lab, Korea University)
- Shared by [Optional]: Jinhyuk Lee
- Model type: Feature Extraction
- Language(s) (NLP): More information needed
- License: More information needed
- 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.
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: More information needed
- 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-bc5cdr-disease")
model = AutoModel.from_pretrained("dmis-lab/biosyn-sapbert-bc5cdr-disease")