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

[![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 `hingeLoss` 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** hingeLoss

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-MiniLM-L12-Hinge")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-MiniLM-L12-Hinge")

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        | 38.68     | 45.16     |
| trec2019           | 97.67     | 73.42     |
| trec2020           | 95.06     | 73.72     |
| fever              | 78.87     | 79.00     |
| arguana            | 22.46     | 33.27     |
| climate_fever      | 26.81     | 20.05     |
| dbpedia            | 74.03     | 43.09     |
| fiqa               | 44.61     | 36.41     |
| hotpotqa           | 85.90     | 68.09     |
| nfcorpus           | 56.50     | 33.72     |
| nq                 | 51.79     | 56.76     |
| quora              | 68.98     | 72.31     |
| scidocs            | 27.61     | 15.34     |
| scifact            | 67.59     | 70.06     |
| touche             | 65.41     | 33.09     |
| trec_covid         | 89.35     | 69.05     |
| robust04           | 71.52     | 49.26     |
| lotte_writing      | 66.06     | 57.90     |
| lotte_recreation   | 61.23     | 55.32     |
| lotte_science      | 45.44     | 37.67     |
| lotte_technology   | 52.88     | 44.83     |
| lotte_lifestyle    | 71.60     | 62.20     |
| **Mean In Domain** | **77.14** | **64.10** |
| **BEIR 13**        | **58.45** | **48.48** |
| **LoTTE (OOD)**    | **61.46** | **51.20** |