|
--- |
|
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") |
|
|
|
``` |
|
</details> |
|
|
|
|