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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
[](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 `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** | |