histlux ocr error denoising lrec

---
tags:

• sentence-transformers

• sentence-similarity

• dataset_size:120000

• multilingual

base_model: Alibaba-NLP/gte-multilingual-base
widget:

• source_sentence: Who is filming along?

sentences:
- Wién filmt mat?
- >-
Weider huet den Tatarescu drop higewisen, datt Rumänien durch seng
krichsbedélegong op de 6eite vun den allie'erten 110.000 mann verluer hätt.
- Brambilla 130.08.03 St.

• source_sentence: 'Four potential scenarios could still play out: Jean Asselborn.'

sentences:
- >-
Dann ass nach eng Antenne hei um Kierchbierg virgesi Richtung RTL Gebai, do
gëtt jo een ganz neie Wunnquartier gebaut.
- >-
D'bedélegong un de wählen wir ganz stärk gewiéscht a munche ge'genden wor re
eso'gucr me' we' 90 prozent.
- Jean Asselborn gesäit 4 Méiglechkeeten, wéi et kéint virugoen.

• source_sentence: >-

Non-profit organisation Passerell, which provides legal council to refugees
in Luxembourg, announced that it has to make four employees redundant in
August due to a lack of funding.
sentences:
- Oetringen nach Remich....8.20» 215»
- >-
D'ASBL Passerell, déi sech ëm d'Berodung vu Refugiéeën a Saache Rechtsfroe
këmmert, wäert am August mussen hir véier fix Salariéen entloossen.
- D'Regierung huet allerdéngs "just" 180.041 Doudeger verzeechent.

• source_sentence: This regulation was temporarily lifted during the Covid pandemic.

sentences:
- Six Jours vu New-York si fir d’équipe Girgetti — Debacco
- Dës Reegelung gouf wärend der Covid-Pandemie ausgesat.
- ING-Marathon ouni gréisser Tëschefäll ofgelaf - 18 Leit hospitaliséiert.

• source_sentence: The cross-border workers should also receive more wages.

sentences:
- D'grenzarbechetr missten och me' lo'n kre'en.
- >-
De Néckel: Firun! Dât ass jo ailes, wèll 't get dach neischt un der Bréck
gemâcht!
- >-
D'Grande-Duchesse Josephine Charlotte an hir Ministeren hunn d'Land
verlooss, et war den Optakt vun der Zäit am Exil.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
model-index:

• name: >-

SentenceTransformer based on
Alibaba-NLP/gte-multilingual-base
results:
- task:
type: contemporary-lb
name: Contemporary-lb
dataset:
name: Contemporary-lb
type: contemporary-lb
metrics:
- type: accuracy
value: 0.6216
name: SIB-200(LB) accuracy
- type: accuracy
value: 0.6282
name: ParaLUX accuracy
- task:
type: bitext-mining
name: LBHistoricalBitextMining
dataset:
name: LBHistoricalBitextMining
type: lb-en
metrics:
- type: accuracy
value: 0.9683
name: LB<->FR accuracy
- type: accuracy
value: 0.9715
name: LB<->EN accuracy
- type: mean_accuracy
value: 0.9793
name: LB<->DE accuracy
license: agpl-3.0
datasets:

• impresso-project/HistLuxAlign

• fredxlpy/LuxAlign

language:

• lb

---

THIS IS A PREVIEW MODEL for the IMPRESSO HALLOWEEN WORKSHOP

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-multilingual-base further adapted to support Historical and Contemporary Luxembourgish. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for (cross-lingual) semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

This model is specialised to perform cross-lingual semantic search to and from Historical/Contemporary Luxembourgish. This model would be particularly useful for libraries and archives that want to perform semantic search and longitudinal studies within their collections.

This is an Alibaba-NLP/gte-multilingual-base model that was further adapted by (Michail et al., 2025)

Limitations

We also release a model that performs better (18pp) on ParaLUX. If finding monolingual exact matches within adversarial collections is of at-most importance, please use histlux-paraphrase-multilingual-mpnet-base-v2

Model Description

• Model Type: GTE-Multilingual-Base

• Base model: Alibaba-NLP/gte-multilingual-base

• Maximum Sequence Length: 8192 tokens

• Output Dimensionality: 768 dimensions

• Similarity Function: Cosine Similarity

• Training Dataset: See below

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

Then you can use the model like this:

Training Details

Training Dataset

The parallel sentences data mix is the following:

impresso-project/HistLuxAlign:
- LB-FR (x20,000)
- LB-EN (x20,000)
- LB-DE (x20,000)

fredxlpy/LuxAlign:
- LB-FR (x40,000)
- LB-EN (x20,000)

Total: 120 000 Sentence pairs in mixed batches of size 8

Contrastive Training

The model was trained with the parameters:
{'scale': 20.0, 'similarity_fct': 'cos_sim'}

{
"epochs": 1,
"evaluation_steps": 520,
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
}

Citation

BibTeX

#### Adapting Multilingual Embedding Models to Historical Luxembourgish (introducing paper)

#### Original Multilingual GTE Model

About Impresso

Impresso project

Impresso - Media Monitoring of the Past is an interdisciplinary research project that aims to develop and consolidate tools for processing and exploring large collections of media archives across modalities, time, languages and national borders. The first project (2017-2021) was funded by the Swiss National Science Foundation under grant No. CRSII5_173719 and the second project (2023-2027) by the SNSF under grant No. [CRSII5_213