Dpr Question Encoder Multiset Base

🚀 dpr-question_encoder-multiset-base

dpr-question_encoder-multiset-base is a question encoder in the DPR framework, trained on multiple datasets for open - domain question answering tasks.

🚀 Quick Start

Use the code below to get started with the model.

from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer

tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base")
model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base")
input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="pt")["input_ids"]
embeddings = model(input_ids).pooler_output

✨ Features

Model Details

Model Description: Dense Passage Retrieval (DPR) is a set of tools and models for state - of - the - art open - domain Q&A research. dpr-question_encoder-multiset-base is the question encoder trained using the Natural Questions (NQ) dataset, TriviaQA, WebQuestions (WQ), and CuratedTREC (TREC).

Uses

Direct Use

dpr-question_encoder-multiset-base, dpr-ctx_encoder-multiset-base, and dpr-reader-multiset-base can be used for the task of open - domain question answering.

Misuse and Out - of - scope Use

The model should not be used to intentionally create hostile or alienating environments for people. In addition, the set of DPR models was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out - of - scope for the abilities of this model.

Risks, Limitations and Biases

⚠️ Important Note

CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.

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 can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Training

Training Data

This model was trained using the following datasets:

• Natural Questions (NQ) dataset (Lee et al., 2019; Kwiatkowski et al., 2019)
• TriviaQA (Joshi et al., 2017)
• WebQuestions (WQ) (Berant et al., 2013)
• CuratedTREC (TREC) (Baudiš & Šedivý, 2015)

Training Procedure

The training procedure is described in the associated paper:

Given a collection of M text passages, the goal of our dense passage retriever (DPR) is to index all the passages in a low - dimensional and continuous space, such that it can retrieve efficiently the top k passages relevant to the input question for the reader at run - time.

Our dense passage retriever (DPR) uses a dense encoder EP(·) which maps any text passage to a d - dimensional real - valued vectors and builds an index for all the M passages that we will use for retrieval. At run - time, DPR applies a different encoder EQ(·) that maps the input question to a d - dimensional vector, and retrieves k passages of which vectors are the closest to the question vector.

The authors report that for encoders, they used two independent BERT (Devlin et al., 2019) networks (base, un - cased) and use FAISS (Johnson et al., 2017) during inference time to encode and index passages. See the paper for further details on training, including encoders, inference, positive and negative passages, and in - batch negatives.

Evaluation

Testing Data, Factors and Metrics

The model developers report the performance of the model on five QA datasets, using the top - k accuracy (k ∈ {20, 100}). The datasets were NQ, TriviaQA, WebQuestions (WQ), CuratedTREC (TREC), and SQuAD v1.1.

Results

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). We present the hardware type and based on the associated paper.

• Hardware Type: 8 32GB GPUs
• Hours used: Unknown
• Cloud Provider: Unknown
• Compute Region: Unknown
• Carbon Emitted: Unknown

Technical Specifications

See the associated paper for details on the modeling architecture, objective, compute infrastructure, and training details.

📄 License

This model is released under the CC - BY - NC - 4.0 license.

📚 Documentation

Citation Information

  @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",
}

Model Card Authors

This model card was written by the team at Hugging Face.