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language: en
license: apache-2.0
library_name: transformers
base_model: jhu-clsp/ettin-encoder-68m
model_name: cross-encoder-ettin-68m-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-68m-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-68m`. 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-68m-DistillRankNET")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ettin-68m-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 | 34.13 | 40.46 |
| trec2019 | 98.84 | 74.07 |
| trec2020 | 91.51 | 71.92 |
| fever | 74.80 | 75.23 |
| arguana | 14.35 | 21.10 |
| climate_fever | 16.46 | 12.19 |
| dbpedia | 71.85 | 42.73 |
| fiqa | 41.85 | 34.25 |
| hotpotqa | 84.44 | 66.11 |
| nfcorpus | 53.60 | 32.11 |
| nq | 48.74 | 53.78 |
| quora | 76.33 | 78.23 |
| scidocs | 23.82 | 12.96 |
| scifact | 58.10 | 60.19 |
| touche | 60.96 | 35.49 |
| trec_covid | 91.02 | 75.07 |
| robust04 | 66.32 | 42.11 |
| lotte_writing | 70.84 | 61.51 |
| lotte_recreation | 57.85 | 52.52 |
| lotte_science | 48.94 | 40.54 |
| lotte_technology | 52.33 | 43.25 |
| lotte_lifestyle | 69.70 | 60.54 |
| **Mean In Domain** | **74.83** | **62.15** |
| **BEIR 13** | **55.10** | **46.11** |
| **LoTTE (OOD)** | **61.00** | **50.08** | |