model documentation

README.md

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+---
+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>