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README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+base_model: microsoft/deberta-v3-base
+model_name: cross-encoder-DeBERTav3-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-DeBERTav3-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 `microsoft/deberta-v3-base`. 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("microsoft/deberta-v3-base")
+model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-DeBERTav3-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        | 35.30     | 41.91     |
+| trec2019           | 94.65     | 74.18     |
+| trec2020           | 93.58     | 70.05     |
+| fever              | 82.83     | 81.97     |
+| arguana            | 13.59     | 21.15     |
+| climate_fever      | 26.82     | 19.27     |
+| dbpedia            | 72.24     | 42.56     |
+| fiqa               | 42.94     | 35.84     |
+| hotpotqa           | 78.51     | 60.35     |
+| nfcorpus           | 47.19     | 28.16     |
+| nq                 | 52.10     | 57.12     |
+| quora              | 71.72     | 74.00     |
+| scidocs            | 25.04     | 14.36     |
+| scifact            | 63.12     | 65.74     |
+| touche             | 68.90     | 34.59     |
+| trec_covid         | 89.07     | 76.15     |
+| robust04           | 70.29     | 46.92     |
+| lotte_writing      | 67.04     | 57.94     |
+| lotte_recreation   | 61.21     | 55.99     |
+| lotte_science      | 48.10     | 40.20     |
+| lotte_technology   | 55.99     | 46.36     |
+| lotte_lifestyle    | 74.51     | 64.85     |
+| **Mean In Domain** | **74.51** | **62.05** |
+| **BEIR 13**        | **56.47** | **47.02** |
+| **LoTTE (OOD)**    | **62.86** | **52.04** |