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language: en
license: apache-2.0
library_name: transformers
base_model: jhu-clsp/ettin-encoder-150m
model_name: cross-encoder-ettin-150m-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-ettin-150m-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 `jhu-clsp/ettin-encoder-150m`. 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("xpmir/cross-encoder-ettin-150m-DistillRankNET")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ettin-150m-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 | 36.30 | 42.93 |
| trec2019 | 96.98 | 75.59 |
| trec2020 | 93.83 | 72.27 |
| fever | 80.10 | 79.82 |
| arguana | 14.52 | 22.21 |
| climate_fever | 27.00 | 19.76 |
| dbpedia | 75.55 | 45.75 |
| fiqa | 47.54 | 39.63 |
| hotpotqa | 85.28 | 66.73 |
| nfcorpus | 57.92 | 35.41 |
| nq | 53.64 | 58.68 |
| quora | 75.29 | 77.46 |
| scidocs | 28.04 | 15.73 |
| scifact | 68.06 | 70.51 |
| touche | 66.31 | 36.81 |
| trec_covid | 96.50 | 77.81 |
| robust04 | 73.96 | 49.37 |
| lotte_writing | 73.65 | 64.31 |
| lotte_recreation | 62.02 | 56.42 |
| lotte_science | 51.11 | 42.43 |
| lotte_technology | 56.74 | 47.53 |
| lotte_lifestyle | 72.68 | 63.49 |
| **Mean In Domain** | **75.70** | **63.60** |
| **BEIR 13** | **59.67** | **49.72** |
| **LoTTE (OOD)** | **65.03** | **53.92** | |