---
language: en
license: apache-2.0
library_name: transformers
base_model: microsoft/MiniLM-L12-H384-uncased
model_name: cross-encoder-MiniLM-L12-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-MiniLM-L12-DistillRankNET

[![Paper](https://img.shields.io/badge/Paper-Arxiv-red)](http://arxiv.org/abs/2603.03010)
[![All Models](https://img.shields.io/badge/🤗%20Hugging%20Face%20Models-blue)](https://huggingface.co/collections/xpmir/reproducing-cross-encoders)
[![GitHub](https://img.shields.io/badge/GitHub-Code-blue)](https://github.com/xpmir/cross-encoders)

This model is a cross-encoder based on `microsoft/MiniLM-L12-H384-uncased`. 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/MiniLM-L12-H384-uncased")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-MiniLM-L12-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        | 37.40     | 43.98     |
| trec2019           | 96.12     | 74.57     |
| trec2020           | 93.83     | 73.48     |
| fever              | 81.21     | 80.95     |
| arguana            | 18.48     | 27.97     |
| climate_fever      | 27.52     | 20.31     |
| dbpedia            | 75.81     | 46.06     |
| fiqa               | 43.71     | 36.25     |
| hotpotqa           | 85.35     | 66.48     |
| nfcorpus           | 57.75     | 34.59     |
| nq                 | 53.19     | 58.21     |
| quora              | 76.34     | 78.62     |
| scidocs            | 28.06     | 15.79     |
| scifact            | 66.12     | 69.34     |
| touche             | 64.33     | 34.46     |
| trec_covid         | 87.17     | 70.74     |
| robust04           | 75.25     | 52.28     |
| lotte_writing      | 66.66     | 58.11     |
| lotte_recreation   | 60.60     | 55.12     |
| lotte_science      | 46.01     | 38.34     |
| lotte_technology   | 53.36     | 44.41     |
| lotte_lifestyle    | 71.62     | 61.69     |
| **Mean In Domain** | **75.78** | **64.01** |
| **BEIR 13**        | **58.85** | **49.21** |
| **LoTTE (OOD)**    | **62.25** | **51.66** |