transformers pytorch bert feature-extraction arxiv:1901.08746 arxiv:1910.09700 text-embeddings-inference endpoints_compatible region:us
---
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>
 
