readme: ---
  language:
  - en
  
  ---
  # 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](https://github.com/castorini/pyserini) 
      - [Associated Paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3463238) 
   
  # Uses
   
   
  ## Direct Use
   
  More information needed
   
  ## Downstream Use [Optional]
   
  More information needed
   
  ## 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)](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
   
  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](https://dl.acm.org/doi/pdf/10.1145/3404835.3463238) 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
   
  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:** 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
  More information needed
   
  ## Compute Infrastructure
   
  More information needed
   
  ### Hardware
   
  More information needed
   
  ### 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:**
   
  ```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]
   
  More information needed
   
  # More Information [optional]
   
  More information needed
   
  # Model Card Authors [optional]
   
  Castorini 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, AnceEncoder
   
  tokenizer = AutoTokenizer.from_pretrained("castorini/ance-msmarco-passage")
   
  model = AnceEncoder.from_pretrained("castorini/ance-msmarco-passage")
   ```
  </details>
  