---
language:
- en
license: cc-by-nc-4.0
---
# Model Card for bert-small-mm_retrieval-passage_encoder
 
 
# Model Details
 
## Model Description
 
Multilingual DPR Model base on bert-base-multilingual-cased.
 
- **Developed by:** Deepset
- **Shared by [Optional]:** Hugging Face
- **Model type:** dpr
- **Language(s) (NLP):** english
- **License:** CC-BY-NC 4.0 
- **Related Models:**
  - **Parent Model:** DPR
- **Resources for more information:** 
    - [GitHub Repo](https://github.com/facebookresearch/DPR)
    - [Associated Paper](https://arxiv.org/abs/2004.04906)
 	
 
# Uses
 
 
## Direct Use
 
This model can be used for the task of Question Answering
 
## 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 English Wikipedia dump from Dec. 20, 2018 as the source documents for answering questions
 
## Training Procedure
 
 ### Preprocessing
 
The model creators note in the [associated paper](https://arxiv.org/pdf/2004.04906.pdf)
> 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. 
 
### Speeds, Sizes, Times
 
More information needed
 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
 
More information needed
 
### Factors
 
 
### 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:** 8 x 32GB GPUs
- **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
 
DPRContextEncoder
 
## Compute Infrastructure
 
More information needed
 
### Hardware
 
More information needed
 
### Software
transformers_version: 4.7.0
 
# Citation
 
 
**BibTeX:**
 ```
@inproceedings{karpukhin-etal-2020-dense,
    title = "Dense Passage Retrieval for Open-Domain Question Answering",
    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",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
    doi = "10.18653/v1/2020.emnlp-main.550",
    pages = "6769--6781",
}
 
```
 
 
# Glossary [optional]
More information needed
 
# More Information [optional]
 
More information needed
 
# Model Card Authors [optional]
 
 
Deepset 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, DPRContextEncoder
 
tokenizer = AutoTokenizer.from_pretrained("deepset/bert-small-mm_retrieval-passage_encoder")
 
model = DPRContextEncoder.from_pretrained("deepset/bert-small-mm_retrieval-passage_encoder")
 
```
</details>