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:
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) and Bender et al. (2021)). 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
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 presented in Lacoste et al. (2019).
- 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.
Click to expand
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")