
# 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>
 

{"tags": ["bert"]}
transformers pytorch bert feature-extraction arxiv:1901.08746 arxiv:1910.09700 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #region-us 


# 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
  - 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: 
- 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
 
BibTeX:
 
  
# Glossary [optional]
 
More information needed
 
# More Information [optional]
 
For help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee('URL (at) URL'), or Wonjin Yoon ('URL (at) URL') 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>


</details>
 

# 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
  - 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: 
- 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
 
BibTeX: # Glossary [optional]
 
More information needed # More Information [optional]
 
For help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee('URL (at) URL'), or Wonjin Yoon ('URL (at) URL') 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>


</details>
TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #region-us 
 # 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
  - 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: 
- 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
 
BibTeX: # Glossary [optional]
 
More information needed # More Information [optional]
 
For help or issues using BioBERT, please submit a GitHub issue. Please contact Jinhyuk Lee('URL (at) URL'), or Wonjin Yoon ('URL (at) URL') 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>


</details>
passage: TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-1901.08746 #arxiv-1910.09700 #endpoints_compatible #region-us 
# 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
  - 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