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.
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]
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.
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
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