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language: en
license: apache-2.0
library_name: transformers
base_model: google/electra-base-discriminator
model_name: cross-encoder-ELECTRA-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-ELECTRA-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 `google/electra-base-discriminator`. 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("google/electra-base-discriminator")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ELECTRA-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 | 37.50 | 44.08 |
| trec2019 | 100.00 | 77.88 |
| trec2020 | 95.00 | 74.82 |
| fever | 79.89 | 80.03 |
| arguana | 15.87 | 24.53 |
| climate_fever | 22.70 | 17.38 |
| dbpedia | 77.35 | 47.24 |
| fiqa | 46.89 | 38.68 |
| hotpotqa | 86.53 | 67.52 |
| nfcorpus | 55.78 | 34.33 |
| nq | 55.00 | 60.02 |
| quora | 77.07 | 79.32 |
| scidocs | 27.87 | 15.98 |
| scifact | 62.64 | 65.76 |
| touche | 68.69 | 35.77 |
| trec_covid | 87.97 | 70.20 |
| robust04 | 70.36 | 49.20 |
| lotte_writing | 70.07 | 61.35 |
| lotte_recreation | 62.44 | 56.76 |
| lotte_science | 47.24 | 40.02 |
| lotte_technology | 55.93 | 47.04 |
| lotte_lifestyle | 74.60 | 64.90 |
| **Mean In Domain** | **77.50** | **65.59** |
| **BEIR 13** | **58.79** | **48.98** |
| **LoTTE (OOD)** | **63.44** | **53.21** | |