model documentation (#3)

- model documentation (3ca86dcaacef6855a6826741880bc575408768de)


Co-authored-by: Nazneen Rajani <[email protected]>

README.md

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+---
+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>
+