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README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+base_model: bert-base-uncased
+model_name: cross-encoder-bert-base-ADR-MSE
+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-ADR-MSE
+
+[![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 `ADR` 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** ADR
+
+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("bert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-bert-base-ADR-MSE")
+
+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        | 36.50     | 42.98     |
+| trec2019           | 97.29     | 74.07     |
+| trec2020           | 92.87     | 71.74     |
+| fever              | 81.06     | 81.04     |
+| arguana            | 23.00     | 34.49     |
+| climate_fever      | 27.78     | 20.52     |
+| dbpedia            | 76.55     | 46.14     |
+| fiqa               | 42.55     | 34.79     |
+| hotpotqa           | 90.03     | 73.39     |
+| nfcorpus           | 55.59     | 34.20     |
+| nq                 | 53.32     | 58.11     |
+| quora              | 80.84     | 82.20     |
+| scidocs            | 28.26     | 15.66     |
+| scifact            | 66.07     | 69.12     |
+| touche             | 62.66     | 33.81     |
+| trec_covid         | 84.83     | 65.90     |
+| robust04           | 70.37     | 48.02     |
+| lotte_writing      | 65.26     | 56.58     |
+| lotte_recreation   | 58.83     | 53.28     |
+| lotte_science      | 43.66     | 36.67     |
+| lotte_technology   | 50.56     | 42.04     |
+| lotte_lifestyle    | 68.66     | 59.78     |
+| **Mean In Domain** | **75.55** | **62.93** |
+| **BEIR 13**        | **59.43** | **49.95** |
+| **LoTTE (OOD)**    | **59.56** | **49.39** |