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language: en
license: apache-2.0
library_name: transformers
base_model: microsoft/deberta-v3-base
model_name: cross-encoder-DeBERTav3-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-DeBERTav3-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 `microsoft/deberta-v3-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("microsoft/deberta-v3-base")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-DeBERTav3-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 | 35.30 | 41.91 |
| trec2019 | 94.65 | 74.18 |
| trec2020 | 93.58 | 70.05 |
| fever | 82.83 | 81.97 |
| arguana | 13.59 | 21.15 |
| climate_fever | 26.82 | 19.27 |
| dbpedia | 72.24 | 42.56 |
| fiqa | 42.94 | 35.84 |
| hotpotqa | 78.51 | 60.35 |
| nfcorpus | 47.19 | 28.16 |
| nq | 52.10 | 57.12 |
| quora | 71.72 | 74.00 |
| scidocs | 25.04 | 14.36 |
| scifact | 63.12 | 65.74 |
| touche | 68.90 | 34.59 |
| trec_covid | 89.07 | 76.15 |
| robust04 | 70.29 | 46.92 |
| lotte_writing | 67.04 | 57.94 |
| lotte_recreation | 61.21 | 55.99 |
| lotte_science | 48.10 | 40.20 |
| lotte_technology | 55.99 | 46.36 |
| lotte_lifestyle | 74.51 | 64.85 |
| **Mean In Domain** | **74.51** | **62.05** |
| **BEIR 13** | **56.47** | **47.02** |
| **LoTTE (OOD)** | **62.86** | **52.04** | |