model documentation

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+ ---
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+ tags:
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+ - question-answering
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+ - bert
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+ ---
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+
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+ # Model Card for biobert-large-cased-v1.1-squad
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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]:** DMIS-lab (Data Mining and Information Systems Lab, Korea University)
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+ - **Model type:** Question Answering
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+ - **Language(s) (NLP):** More information needed
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+ - **License:** More information needed
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+ - **Parent Model:** [gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B)
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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 question answering.
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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:** More information needed
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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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+ ```bibtex
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+ @misc{mesh-transformer-jax,
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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, AutoModelForQuestionAnswering
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+ tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-large-cased-v1.1-squad")
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+ model = AutoModelForQuestionAnswering.from_pretrained("dmis-lab/biobert-large-cased-v1.1-squad")
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+ ```
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+ </details>
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