metadata
language: en
license: apache-2.0
library_name: transformers
base_model: bert-base-uncased
model_name: cross-encoder-bert-base-ADR-MSE
source: https://github.com/xpmir/cross-encoders
paper: http://arxiv.org/abs/2603.03010
tags:
- cross-encoder
- sequence-classification
- tensorboard
datasets:
- msmarco
pipeline_tag: text-classification
cross-encoder-bert-base-ADR-MSE
This model is a cross-encoder based on bert-base-uncased. It was trained on Ms-Marco using loss ADR as part of a reproducibility paper for training cross encoders: "Reproducing and Comparing Distillation Techniques for Cross-Encoders", see the paper for more details.
Contents
Model Description
This model is intended for re-ranking the top results returned by a retrieval system (like BM25, Bi-Encoders or SPLADE).
- Training Data: MS MARCO Passage
- Language: English
- Loss ADR
Training can be easily reproduced using the assiciated repository. The exact training configuration used for this model is also detailed in config.yaml.
Usage
Quick Start:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-bert-base-ADR-MSE")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-bert-base-ADR-MSE")
features = tokenizer("What is experimaestro ?", "Experimaestro is a powerful framework for ML experiments management...", padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
Evaluations
We provide evaluations of this cross-encoder re-ranking the top 1000 documents retrieved by naver/splade-v3-distilbert.
| dataset | RR@10 | nDCG@10 |
|---|---|---|
| msmarco_dev | 36.50 | 42.98 |
| trec2019 | 97.29 | 74.07 |
| trec2020 | 92.87 | 71.74 |
| fever | 81.06 | 81.04 |
| arguana | 23.00 | 34.49 |
| climate_fever | 27.78 | 20.52 |
| dbpedia | 76.55 | 46.14 |
| fiqa | 42.55 | 34.79 |
| hotpotqa | 90.03 | 73.39 |
| nfcorpus | 55.59 | 34.20 |
| nq | 53.32 | 58.11 |
| quora | 80.84 | 82.20 |
| scidocs | 28.26 | 15.66 |
| scifact | 66.07 | 69.12 |
| touche | 62.66 | 33.81 |
| trec_covid | 84.83 | 65.90 |
| robust04 | 70.37 | 48.02 |
| lotte_writing | 65.26 | 56.58 |
| lotte_recreation | 58.83 | 53.28 |
| lotte_science | 43.66 | 36.67 |
| lotte_technology | 50.56 | 42.04 |
| lotte_lifestyle | 68.66 | 59.78 |
| Mean In Domain | 75.55 | 62.93 |
| BEIR 13 | 59.43 | 49.95 |
| LoTTE (OOD) | 59.56 | 49.39 |