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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-32m
+model_name: cross-encoder-ettin-32m-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-32m-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-32m`. 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-32m")
+model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-ettin-32m-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        | 29.69     | 35.29     |
+| trec2019           | 91.86     | 62.04     |
+| trec2020           | 85.57     | 63.47     |
+| fever              | 70.41     | 71.33     |
+| arguana            | 8.61      | 13.20     |
+| climate_fever      | 16.04     | 11.98     |
+| dbpedia            | 61.21     | 34.43     |
+| fiqa               | 32.94     | 25.37     |
+| hotpotqa           | 74.34     | 57.33     |
+| nfcorpus           | 40.43     | 23.10     |
+| nq                 | 38.18     | 42.81     |
+| quora              | 72.61     | 73.97     |
+| scidocs            | 21.50     | 11.66     |
+| scifact            | 51.45     | 54.28     |
+| touche             | 64.88     | 31.23     |
+| trec_covid         | 88.83     | 64.72     |
+| robust04           | 52.38     | 31.19     |
+| lotte_writing      | 59.75     | 50.70     |
+| lotte_recreation   | 48.66     | 43.92     |
+| lotte_science      | 38.10     | 32.33     |
+| lotte_technology   | 42.30     | 34.81     |
+| lotte_lifestyle    | 59.83     | 50.72     |
+| **Mean In Domain** | **69.04** | **53.60** |
+| **BEIR 13**        | **49.34** | **39.65** |
+| **LoTTE (OOD)**    | **50.17** | **40.61** |