---
language: en
license: apache-2.0
library_name: transformers
base_model: bert-base-uncased
model_name: cross-encoder-bert-base-infoNCE
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-bert-base-infoNCE

[![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 `bert-base-uncased`. It was trained on Ms-Marco using loss `infoNCE` 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** infoNCE

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-bert-base-infoNCE")
model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-bert-base-infoNCE")

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        | 40.13     | 46.68     |
| trec2019           | 98.26     | 75.65     |
| trec2020           | 93.36     | 73.30     |
| fever              | 81.43     | 81.33     |
| arguana            | 23.01     | 34.22     |
| climate_fever      | 31.31     | 23.24     |
| dbpedia            | 78.14     | 45.69     |
| fiqa               | 42.83     | 35.87     |
| hotpotqa           | 89.63     | 73.49     |
| nfcorpus           | 55.04     | 34.24     |
| nq                 | 54.25     | 59.13     |
| quora              | 78.34     | 80.38     |
| scidocs            | 26.07     | 15.06     |
| scifact            | 69.26     | 71.47     |
| touche             | 61.20     | 33.31     |
| trec_covid         | 90.57     | 67.53     |
| robust04           | 71.40     | 48.48     |
| lotte_writing      | 68.55     | 58.87     |
| lotte_recreation   | 59.75     | 54.52     |
| lotte_science      | 43.67     | 36.39     |
| lotte_technology   | 51.72     | 42.85     |
| lotte_lifestyle    | 71.37     | 61.89     |
| **Mean In Domain** | **77.25** | **65.21** |
| **BEIR 13**        | **60.08** | **50.38** |
| **LoTTE (OOD)**    | **61.08** | **50.50** |