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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
[](http://arxiv.org/abs/2603.03010)
[](https://huggingface.co/collections/xpmir/reproducing-cross-encoders)
[](https://github.com/xpmir/cross-encoders)
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](http://arxiv.org/abs/2603.03010)**", see the paper for more details.
### Contents
- [Model Description](#model-description)
- [Usage](#usage)
- [Evals](#evaluations)
## 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](./config.yaml).
## Usage
Quick Start:
```python
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** | |