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--- |
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tags: |
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- bert |
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--- |
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# Model Card for biosyn-sapbert-ncbi-disease |
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# Model Details |
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## Model Description |
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More information needed |
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- **Developed by:** Dmis-lab (Data Mining and Information Systems Lab, Korea University) |
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- **Shared by [Optional]:** Hugging Face |
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- **Model type:** Feature Extraction |
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- **Language(s) (NLP):** More information needed |
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- **License:** More information needed |
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- **Related Models:** |
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- **Parent Model:** BERT |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/jhyuklee/biobert) |
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- [Associated Paper](https://arxiv.org/abs/1901.08746) |
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# Uses |
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## Direct Use |
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This model can be used for the task of Feature Extraction |
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## Downstream Use [Optional] |
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More information needed |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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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. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf) |
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> 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)) |
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## Training Procedure |
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### Preprocessing |
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The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf) |
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> 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 |
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### Speeds, Sizes, Times |
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The model creators note in the [associated paper](https://arxiv.org/pdf/1901.08746.pdf) |
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> The maximum sequence length was fixed to 512 and the mini-batch size was set to 192, resulting in 98 304 words per iteration. |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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More information needed |
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### Factors |
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More information needed |
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### Metrics |
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More information needed |
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## Results |
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More information needed |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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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). |
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- **Hardware Type:** |
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- **Training:** Eight NVIDIA V100 (32GB) GPUs [ for training], |
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- **Fine-tuning:** a single NVIDIA Titan Xp (12GB) GPU to fine-tune BioBERT on each task |
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- **Hours used:** More information needed |
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- **Cloud Provider:** More information needed |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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More information needed |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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More information needed |
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### Software |
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More information needed |
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# Citation |
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**BibTeX:** |
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``` |
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@article{lee2019biobert, |
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title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining}, |
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author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo}, |
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journal={arXiv preprint arXiv:1901.08746}, |
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year={2019} |
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} |
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``` |
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# Glossary [optional] |
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More information needed |
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# More Information [optional] |
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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. |
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# Model Card Authors [optional] |
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Dmis-lab (Data Mining and Information Systems Lab, Korea University) in collaboration with Ezi Ozoani and the Hugging Face team |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biosyn-sapbert-ncbi-disease") |
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model = AutoModel.from_pretrained("dmis-lab/biosyn-sapbert-ncbi-disease") |
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``` |
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</details> |
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