---
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>