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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-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-bert-base-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 `bert-base-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("bert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-bert-base-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        | 36.42     | 42.84     |
+| trec2019           | 95.74     | 74.15     |
+| trec2020           | 94.25     | 72.10     |
+| fever              | 81.04     | 80.99     |
+| arguana            | 22.80     | 34.31     |
+| climate_fever      | 29.17     | 21.50     |
+| dbpedia            | 76.58     | 45.80     |
+| fiqa               | 43.41     | 35.34     |
+| hotpotqa           | 89.45     | 72.86     |
+| nfcorpus           | 56.85     | 34.36     |
+| nq                 | 52.57     | 57.27     |
+| quora              | 76.95     | 78.94     |
+| scidocs            | 28.31     | 15.65     |
+| scifact            | 67.81     | 70.21     |
+| touche             | 63.22     | 34.36     |
+| trec_covid         | 89.83     | 68.52     |
+| robust04           | 69.69     | 47.75     |
+| lotte_writing      | 64.88     | 55.85     |
+| lotte_recreation   | 58.11     | 52.84     |
+| lotte_science      | 43.32     | 36.06     |
+| lotte_technology   | 49.62     | 41.06     |
+| lotte_lifestyle    | 70.00     | 60.53     |
+| **Mean In Domain** | **75.47** | **63.03** |
+| **BEIR 13**        | **59.85** | **50.01** |
+| **LoTTE (OOD)**    | **59.27** | **49.01** |