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language: en
license: apache-2.0
library_name: transformers
base_model: bert-base-uncased
model_name: cross-encoder-bert-base-BCE
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-BCE
[](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 `bce` 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** bce
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-BCE")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-bert-base-BCE")
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 | 37.63 | 44.00 |
| trec2019 | 90.00 | 67.38 |
| trec2020 | 91.96 | 68.39 |
| fever | 76.49 | 77.27 |
| arguana | 21.41 | 32.09 |
| climate_fever | 33.26 | 24.32 |
| dbpedia | 71.92 | 41.65 |
| fiqa | 42.57 | 34.34 |
| hotpotqa | 86.45 | 70.63 |
| nfcorpus | 49.72 | 27.88 |
| nq | 51.49 | 56.28 |
| quora | 71.56 | 74.43 |
| scidocs | 24.84 | 13.74 |
| scifact | 63.67 | 66.02 |
| touche | 61.83 | 32.49 |
| trec_covid | 84.43 | 58.66 |
| robust04 | 66.34 | 42.61 |
| lotte_writing | 66.37 | 57.13 |
| lotte_recreation | 57.83 | 52.25 |
| lotte_science | 41.88 | 35.02 |
| lotte_technology | 50.35 | 41.56 |
| lotte_lifestyle | 68.01 | 58.36 |
| **Mean In Domain** | **73.20** | **59.92** |
| **BEIR 13** | **56.90** | **46.91** |
| **LoTTE (OOD)** | **58.46** | **47.82** | |