metadata
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
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", see the paper for more details.
Contents
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.
Usage
Quick Start:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-ettin-17m-DistillRankNET")
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 |