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--- |
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language: |
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- en |
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license: cc-by-nc-4.0 |
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--- |
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# Model Card for bert-small-mm_retrieval-passage_encoder |
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# Model Details |
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## Model Description |
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Multilingual DPR Model base on bert-base-multilingual-cased. |
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- **Developed by:** Deepset |
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- **Shared by [Optional]:** Hugging Face |
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- **Model type:** dpr |
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- **Language(s) (NLP):** english |
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- **License:** CC-BY-NC 4.0 |
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- **Related Models:** |
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- **Parent Model:** DPR |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/facebookresearch/DPR) |
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- [Associated Paper](https://arxiv.org/abs/2004.04906) |
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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 English Wikipedia dump from Dec. 20, 2018 as the source documents for answering questions |
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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/2004.04906.pdf) |
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> We first apply the pre-processing code released in DrQA (Chen et al., 2017) to extract the clean, text-portion of articles from the Wikipedia dump. |
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### Speeds, Sizes, Times |
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More information needed |
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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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### 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:** 8 x 32GB GPUs |
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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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DPRContextEncoder |
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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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transformers_version: 4.7.0 |
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# Citation |
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**BibTeX:** |
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``` |
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@inproceedings{karpukhin-etal-2020-dense, |
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title = "Dense Passage Retrieval for Open-Domain Question Answering", |
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author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau", |
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booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2020.emnlp-main.550", |
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doi = "10.18653/v1/2020.emnlp-main.550", |
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pages = "6769--6781", |
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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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More information needed |
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# Model Card Authors [optional] |
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Deepset 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, DPRContextEncoder |
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tokenizer = AutoTokenizer.from_pretrained("deepset/bert-small-mm_retrieval-passage_encoder") |
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model = DPRContextEncoder.from_pretrained("deepset/bert-small-mm_retrieval-passage_encoder") |
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``` |
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</details> |
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