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language: |
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- en |
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--- |
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# Model Card for ance-msmarco-passage |
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Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. |
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# Model Details |
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## Model Description |
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Pyserini is primarily designed to provide effective, reproducible, and easy-to-use first-stage retrieval in a multi-stage ranking architecture |
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- **Developed by:** Castorini |
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- **Shared by [Optional]:** Hugging Face |
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- **Model type:** Information retrieval |
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- **Language(s) (NLP):** en |
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- **License:** More information needed |
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- **Related Models:** More information needed |
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- **Parent Model:** RoBERTa |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/castorini/pyserini) |
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- [Associated Paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3463238) |
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# Uses |
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## Direct Use |
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More information needed |
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## Downstream Use [Optional] |
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More information needed |
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## Out-of-Scope Use |
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More information needed |
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# Bias, Risks, and Limitations |
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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. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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More information needed |
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## Training Procedure |
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### Preprocessing |
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More information needed |
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### Speeds, Sizes, Times |
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More information needed |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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The model creators note in the [associated Paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3463238) that: |
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> bag-of-words ranking with BM25 (the default ranking model) on the MS MARCO passage corpus (comprising 8.8M passages) |
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### Factors |
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More information needed |
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### Metrics |
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More information needed |
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## Results |
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More information needed |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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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). |
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- **Hardware Type:** More information needed |
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- **Hours used:** More information needed |
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- **Cloud Provider:** More information needed |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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More information needed |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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More information needed |
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### Software |
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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, |
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# Citation |
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**BibTeX:** |
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```bibtex |
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@INPROCEEDINGS{Lin_etal_SIGIR2021_Pyserini, |
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author = "Jimmy Lin and Xueguang Ma and Sheng-Chieh Lin and Jheng-Hong Yang and Ronak Pradeep and Rodrigo Nogueira", |
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title = "{Pyserini}: A {Python} Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations", |
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booktitle = "Proceedings of the 44th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021)", |
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year = 2021, |
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pages = "2356--2362", |
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} |
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``` |
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# Glossary [optional] |
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More information needed |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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Castorini in collaboration with Ezi Ozoani and the Hugging Face team. |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, AnceEncoder |
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tokenizer = AutoTokenizer.from_pretrained("castorini/ance-msmarco-passage") |
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model = AnceEncoder.from_pretrained("castorini/ance-msmarco-passage") |
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``` |
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</details> |
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