castorini / ance-msmarco-passage

huggingface.co
Total runs: 437
24-hour runs: 0
7-day runs: -390
30-day runs: -794
Model's Last Updated: November 09 2022

Introduction of ance-msmarco-passage

Model Details of ance-msmarco-passage

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:

Uses

Direct Use

More information needed

Downstream Use [Optional]

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Out-of-Scope Use

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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

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Speeds, Sizes, Times

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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.

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

Runs of castorini ance-msmarco-passage on huggingface.co

437
Total runs
0
24-hour runs
-28
3-day runs
-390
7-day runs
-794
30-day runs

More Information About ance-msmarco-passage huggingface.co Model

ance-msmarco-passage huggingface.co

ance-msmarco-passage huggingface.co is an AI model on huggingface.co that provides ance-msmarco-passage's model effect (), which can be used instantly with this castorini ance-msmarco-passage model. huggingface.co supports a free trial of the ance-msmarco-passage model, and also provides paid use of the ance-msmarco-passage. Support call ance-msmarco-passage model through api, including Node.js, Python, http.

ance-msmarco-passage huggingface.co Url

https://huggingface.co/castorini/ance-msmarco-passage

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castorini ance-msmarco-passage online free url in huggingface.co:

https://huggingface.co/castorini/ance-msmarco-passage

ance-msmarco-passage install

ance-msmarco-passage is an open source model from GitHub that offers a free installation service, and any user can find ance-msmarco-passage on GitHub to install. At the same time, huggingface.co provides the effect of ance-msmarco-passage install, users can directly use ance-msmarco-passage installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

ance-msmarco-passage install url in huggingface.co:

https://huggingface.co/castorini/ance-msmarco-passage

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Updated: November 05 2021