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language: en
license: apache-2.0
library_name: transformers
base_model: FacebookAI/roberta-base
model_name: cross-encoder-RoBERTa-DistillRankNET
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-RoBERTa-DistillRankNET
[](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 `FacebookAI/roberta-base`. It was trained on Ms-Marco using loss `distillRankNET` 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** distillRankNET
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("FacebookAI/roberta-base")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-RoBERTa-DistillRankNET")
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 | 35.81 | 42.34 |
| trec2019 | 92.13 | 72.11 |
| trec2020 | 95.43 | 73.13 |
| fever | 79.50 | 79.69 |
| arguana | 17.94 | 26.98 |
| climate_fever | 30.81 | 22.50 |
| dbpedia | 76.74 | 46.32 |
| fiqa | 47.91 | 39.47 |
| hotpotqa | 85.40 | 68.07 |
| nfcorpus | 55.25 | 33.79 |
| nq | 53.82 | 58.72 |
| quora | 79.55 | 81.33 |
| scidocs | 27.23 | 15.38 |
| scifact | 65.37 | 68.74 |
| touche | 62.74 | 34.90 |
| trec_covid | 83.20 | 65.28 |
| robust04 | 71.57 | 48.06 |
| lotte_writing | 68.54 | 58.58 |
| lotte_recreation | 60.93 | 55.18 |
| lotte_science | 46.92 | 38.96 |
| lotte_technology | 52.04 | 43.24 |
| lotte_lifestyle | 72.52 | 62.88 |
| **Mean In Domain** | **74.46** | **62.53** |
| **BEIR 13** | **58.88** | **49.32** |
| **LoTTE (OOD)** | **62.09** | **51.15** | |