Model Card for ance-msmarco-passage

Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.

Model Details

Model Description

Pyserini is primarily designed to provide effective, reproducible, and easy-to-use first-stage retrieval in a multi-stage ranking architecture

• Developed by: Castorini
• Shared by [Optional]: Hugging Face
• Model type: Information retrieval
• Language(s) (NLP): en
• License: More information needed
• Related Models: More information needed
  • Parent Model: RoBERTa
• Resources for more information:
  • GitHub Repo
  • Associated Paper

Uses

Direct Use

More information needed

Downstream Use [Optional]

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Out-of-Scope Use

More information needed

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

More information needed

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model creators note in the associated Paper that:

bag-of-words ranking with BM25 (the default ranking model) on the MS MARCO passage corpus (comprising 8.8M passages)

Factors

More information needed

Metrics

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Results

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Model Examination

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019) .

• Hardware Type: More information needed
• 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

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Compute Infrastructure

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Hardware

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Software

For bag-of-words sparse retrieval, we have built in Anserini (written in Java) custom parsers and ingestion pipelines for common document formats used in IR research,

Citation

BibTeX:

 
@INPROCEEDINGS{Lin_etal_SIGIR2021_Pyserini,
   author = "Jimmy Lin and Xueguang Ma and Sheng-Chieh Lin and Jheng-Hong Yang and Ronak Pradeep and Rodrigo Nogueira",
   title = "{Pyserini}: A {Python} Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations",
   booktitle = "Proceedings of the 44th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021)",
   year = 2021,
   pages = "2356--2362",
}

Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

Castorini in collaboration with Ezi Ozoani and the Hugging Face team.

Model Card Contact

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How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AnceEncoder
 
tokenizer = AutoTokenizer.from_pretrained("castorini/ance-msmarco-passage")
 
model = AnceEncoder.from_pretrained("castorini/ance-msmarco-passage")