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NER-Luxury

Token Classification
Transformers
Safetensors
English
xlm-roberta
NER
Fashion
Luxury
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metadata
extra_gated_prompt: >-
  You agree to not use the model to conduct experiments that cause harm to human
  subjects.
extra_gated_fields:
  Name: text
  AI-Lab/Company: text
  Email: text
  I agree to use this model for academic research (non-commercial use ONLY): checkbox
license: bigscience-openrail-m
language: en
base_model: xlm-roberta-base
tags:
  - NER
  - token-classification
  - Fashion
  - Luxury
library_name: transformers
model-index:
  - name: AkimfromParis/Bert-Luxury
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: Private
          type: private
        metrics:
          - name: Loss
            type: Loss
            value: 0.4079
            verified: true
          - name: Precision
            type: Precision
            value: 0.7652
            verified: true
          - name: Recall
            type: Recall
            value: 0.8033
            verified: true
          - name: F1
            type: F1
            value: 0.7838
            verified: true
          - name: Accuracy
            type: Accuracy
            value: 0.9403
            verified: true
pipeline_tag: token-classification
widget:
  - text: >-
      According to Bloomberg, the market cap of LVMH surpassed $500 billion
      becoming the first European company to reach that milestone. As of July
      2023, Hermès has a market cap of $213.80 Billion, bigger than Nike at
      $161.80 Billion.
    example_title: Finance
  - text: >-
      During Milan Fashion Week, Raf Simons and Miuccia Prada showcased their
      latest Prada collection at the Fondazione Prada in Milano.
    example_title: Fashion
  - text: >-
      On 3 April 2023, L'Oréal acquired for $2.5 Billion the cosmetic label
      Aēsop from Australia.  And on 26 June 2023, the French luxury group Kering
      acquired 100% of the perfume house, Creed from a fund of BlackRock
    example_title: Beauty
  - text: >-
      French house Hermès and British department store Selfridges are leaving
      the Fashion Pact after the appointment of CEO Helena Helmersson from
      Swedish fast-fashion company H&M as the new co-chair
    example_title: Sustainability

NER-Luxury

A fine-tuned XLM-Roberta model for NER in the fashion and luxury industry

. Goal

  • NER-Luxury is a fine-tuned XLM-Roberta model for the subtask N.E.R (Named Entity Recognition) in English. NER-Luxury is domain-specific for the fashion and luxury industry with bespoke labels. NER-Luxury is trying to be a bridge between the aesthetic side and the quantitative side of the fashion and luxury industry.
  • As a downstream task, NER-Luxury is able to identify major fashion houses, artistic directors, fragrances, models, or influential artists on the website of a fashion magazine. And NER-Luxury is also able to identify companies, listed groups, executives, financial analysts, and investment companies inside a 200-page quarterly financial report.
  • The goal of NER-Luxury is to create a clear hierarchical classification of luxury houses, fine watchmakers, beauty brands, sportswear labels, and fast fashion brands with respect of temporality, context, and sustainability. NER-Luxury is trying to solve the "entity disambiguation" between the founder, his eponymous label, the company designation, the names of products, and the intellectual property rights for corporate lawyers, M&A bankers, and financial analysts.

For example, the disambiguation of Louis Vuitton:

  • The visionary founder, Louis Vuitton (1821-1892)
  • The luxury house, Louis Vuitton
  • The giant luxury group LVMH Moët Hennessy Louis Vuitton SE
  • The collection with Japanese artist, Louis Vuitton x Yayoi Kusama

. NER bespoke labels

Entities are evolving according to temporality, and context.

Label Description and example
O Outside (of a text segment)
Date Temporal expressions (1854, Q2 2023, Nineties, September 21)
Location Physical location and area (Paris, Japan, Europe, Champs-Elysées)
Event Critical events (WW II, Olympics, IPO, Covid pandemic, Paris Fashion Week)
MonetaryValue Currency, price, sales, revenue ($2.65 billion, 4.6 million euros, CHF 400,000, etc.)
House Fashion and luxury houses (Louis Vuitton, Cartier, Gucci, Chanel)
Brand Sportswear, beauty and labels (Nike, Lululemon, Clinique)
FastFashion Mass-market retailers (Zara, H&M, Uniqlo, Shein)
PrivateCompany Unlisted companies (Chanel SA, Stella McCartney Ltd, Valentino S.p.A)
ListedGroup Listed groups (LVMH, Hermès International SCA, Kering)
HoldingTrust Holding and family office (Agache, H51, Mousse Partners, Artèmis)
InvestmentFirm Investment banks, PE funds, M&A firms (KKR, L Catterton, Mayhoola, Bernstein)
MediaPublisher Media outlets (Bloomberg, Vogue, Business of Fashion, NYT)
Hospitality Luxury hospitality (Ritz Paris, Belmond hotel Cipriani,Venetian Macao)
MuseumGallery Exhibition spaces (Louvre, MET, Victoria & Albert, Pinault Collection)
Retailer POS, department stores, and select shops (Bergdorf, Le Bon Marché, Takashimaya)
Education Business and fashion schools (Polytechnic, Harvard, LSE, ESCP, Central Saint Martins, IFM)
Organization Legal, scientific, and cultural entities (CFDA, European Union, UNESCO, SEC)
ArtisticDirector Lead creative of houses (Karl Lagerfeld, Daniel Lee, Sarah Burton, Alessandro Michele)
Executive C-level, board members (Jérôme Lambert, Sue Nabi, Pietro Beccari)
Founder Founder, creative, and owner (Ralph Lauren, Rei Kawakubo, Michael Kors)
Chairperson Chairman/Chairwoman (Bernard Arnault, Patrizio Bertelli, François-Henri Pinault)
AnalystBanker Equity analysts, M&A bankers (Luca Solca, Pierre Mallevays, Louise Singlehurst)
KOL Artists, celebrities, historical figures (Audrey Hepburn, BTS, Kanye West, Emma Watson)
AthleteTeam Professional athletes and teams (David Beckham, Maria Sharapova, Luna Rossa, Scuderia Ferrari)
Model Fashion models (Iman, Kate Moss, Adriana Lima, Naomi Campbell, Mariacarla Boscono)
CreativeInsider Photographers, make-up artists, watchmakers (Steven Meisel, Dominique Ropion, Gérald Genta)
EditorJournalist Editor-in-chief, fashion editors, journalists (Suzy Menkes, Anna Wintour, Carine Roitfeld)
GarmCollection Iconic garment and collections (Haute Couture, Bar suit, No.13 of McQueen, Green Jungle Dress)
Cosmetic Cosmetic products (Tilbury Glow palette, Crème de La Mer, YSL Nu, Viva Glam)
Fragrance Perfumes and EdT (Chanel No.5, Dior Sauvage, Terre d'Hermès, Tom Ford Black Orchid)
BagTrvlGoods Bags, handbags, and leather goods (Hermès Birkin bag, Louis Vuitton Speedy bag, Chanel 2.55)
Jewelry Fine jewellery, and gems (Alhambra of Van Cleef & Arpels, Juste un Clou Cartier, The Winston Blue)
Timepiece Fine watches (Nautilus Patek Philippe, Reverso Jaeger-Lecoultre, Rolex Oyster)
Footwear High heels to sneakers (Rainbow of Ferragamo, Armadillo of McQueen, Air Force1)
WineSpirit Wine and spirit (Château d'Yquem, Clos de Tart, Château Matras, Hennessy, Moet, Belvedere)
Sustainability Relevant ESG factors and entities (Ethical Fashion Initiative, decoupling, biodiversity loss)
CulturalArtifact Songs, books, movies (The Devil wears Prada, American Gigolo, Poker Face, The College Dropout)

Paper address and cite information: https://arxiv.org/abs/2409.15804

Citation info 

@misc{mousterou2024nerluxurynamedentityrecognition,
      title={NER-Luxury: Named entity recognition for the fashion and luxury domain}, 
      author={Akim Mousterou},
      year={2024},
      eprint={2409.15804},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.15804}, 
}

How to use NER-Luxury with HuggingFace?

Load NER-Luxury and its sub-word tokenizer : 

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("AkimfromParis/NER-Luxury")
model = AutoModelForTokenClassification.from_pretrained("AkimfromParis/NER-Luxury")
nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

example = "CEO Leena Nair dismisses IPO rumours for Chanel."
ner_results = nlp(example)
print(ner_results)

NER-Luxury

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4079
  • Precision: 0.7652
  • Recall: 0.8033
  • F1: 0.7838
  • Accuracy: 0.9403

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 8
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
1.1269 1.0 1155 0.6237 0.6085 0.6716 0.6385 0.9005
0.5871 2.0 2310 0.4933 0.6857 0.7367 0.7103 0.9208
0.4517 3.0 3465 0.4470 0.7115 0.7639 0.7368 0.9273
0.3692 4.0 4620 0.4271 0.7298 0.7797 0.7539 0.9322
0.3121 5.0 5775 0.4103 0.7422 0.7906 0.7656 0.9362
0.2726 6.0 6930 0.4109 0.7531 0.7940 0.7730 0.9381
0.2138 7.0 8085 0.4088 0.7632 0.8005 0.7814 0.9397
0.1962 8.0 9240 0.4079 0.7652 0.8033 0.7838 0.9403

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1