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README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+base_model: jhu-clsp/ettin-encoder-17m
+model_name: cross-encoder-ettin-17m-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-ettin-17m-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 `jhu-clsp/ettin-encoder-17m`. 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("jhu-clsp/ettin-encoder-17m")
+model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ettin-17m-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        | 23.94     | 28.98     |
+| trec2019           | 80.25     | 53.28     |
+| trec2020           | 86.96     | 53.33     |
+| fever              | 62.54     | 64.01     |
+| arguana            | 8.17      | 12.20     |
+| climate_fever      | 14.74     | 10.74     |
+| dbpedia            | 56.75     | 30.47     |
+| fiqa               | 25.77     | 20.02     |
+| hotpotqa           | 54.49     | 38.89     |
+| nfcorpus           | 44.88     | 24.70     |
+| nq                 | 30.91     | 35.10     |
+| quora              | 72.67     | 73.54     |
+| scidocs            | 14.23     | 7.60      |
+| scifact            | 44.12     | 46.84     |
+| touche             | 57.82     | 30.06     |
+| trec_covid         | 78.54     | 57.16     |
+| robust04           | 51.90     | 30.83     |
+| lotte_writing      | 51.33     | 41.45     |
+| lotte_recreation   | 43.58     | 38.96     |
+| lotte_science      | 33.19     | 27.43     |
+| lotte_technology   | 32.42     | 25.31     |
+| lotte_lifestyle    | 53.95     | 45.13     |
+| **Mean In Domain** | **63.72** | **45.20** |
+| **BEIR 13**        | **43.51** | **34.72** |
+| **LoTTE (OOD)**    | **44.39** | **34.85** |