How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("feature-extraction", model="OpenMatch/ance-tele_triviaqa_psg-encoder")
# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("OpenMatch/ance-tele_triviaqa_psg-encoder")
model = AutoModel.from_pretrained("OpenMatch/ance-tele_triviaqa_psg-encoder")
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This model is the passage encoder of ANCE-Tele trained on TriviaQA, described in the EMNLP 2022 paper "Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives". The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele.

ANCE-Tele only trains with self-mined negatives (teleportation negatives) without using additional negatives (e.g., BM25, other DR systems) and eliminates the dependency on filtering strategies and distillation modules.

NQ (Test) R@5 R@20 R@20
ANCE-Tele 76.9 83.4 87.3
@inproceedings{sun2022ancetele,
  title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives},
  author={Si, Sun and Chenyan, Xiong and Yue, Yu and Arnold, Overwijk and Zhiyuan, Liu and Jie, Bao},
  booktitle={Proceedings of EMNLP 2022},
  year={2022}
}
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Paper for OpenMatch/ance-tele_triviaqa_psg-encoder