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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) |
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
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Base model
FacebookAI/xlm-roberta-base
Finetuned
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Evaluation results
- Loss on Privateself-reported0.408
- Precision on Privateself-reported0.765
- Recall on Privateself-reported0.803
- F1 on Privateself-reported0.784
- Accuracy on Privateself-reported0.940