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--- |
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extra_gated_prompt: >- |
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You agree to not use the model to conduct experiments that cause harm to human |
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subjects. |
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extra_gated_fields: |
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Name: text |
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AI-Lab/Company: text |
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Email: text |
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I agree to use this model for academic research (non-commercial use ONLY): checkbox |
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license: bigscience-openrail-m |
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language: en |
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base_model: xlm-roberta-base |
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tags: |
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- NER |
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- token-classification |
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- Fashion |
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- Luxury |
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library_name: transformers |
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model-index: |
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- name: AkimfromParis/Bert-Luxury |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: Private |
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type: private |
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metrics: |
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- name: Loss |
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type: Loss |
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value: 0.4079 |
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verified: true |
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- name: Precision |
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type: Precision |
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value: 0.7652 |
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verified: true |
|
- name: Recall |
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type: Recall |
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value: 0.8033 |
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verified: true |
|
- name: F1 |
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type: F1 |
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value: 0.7838 |
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verified: true |
|
- name: Accuracy |
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type: Accuracy |
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value: 0.9403 |
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verified: true |
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pipeline_tag: token-classification |
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widget: |
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- text: >- |
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According to Bloomberg, the market cap of LVMH surpassed $500 billion |
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becoming the first European company to reach that milestone. As of July |
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2023, Hermès has a market cap of $213.80 Billion, bigger than Nike at |
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$161.80 Billion. |
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example_title: Finance |
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- text: >- |
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During Milan Fashion Week, Raf Simons and Miuccia Prada showcased their |
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latest Prada collection at the Fondazione Prada in Milano. |
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example_title: Fashion |
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- text: >- |
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On 3 April 2023, L'Oréal acquired for $2.5 Billion the cosmetic label Aēsop |
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from Australia. And on 26 June 2023, the French luxury group Kering |
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acquired 100% of the perfume house, Creed from a fund of BlackRock |
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example_title: Beauty |
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- text: >- |
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French house Hermès and British department store Selfridges are leaving the |
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Fashion Pact after the appointment of CEO Helena Helmersson from Swedish |
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fast-fashion company H&M as the new co-chair |
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example_title: Sustainability |
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--- |
|
|
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# ***NER-Luxury*** |
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# A fine-tuned XLM-Roberta model for NER in the fashion and luxury industry |
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## . Goal |
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|
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- **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. |
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- 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. |
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- 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. |
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|
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For example, the disambiguation of **Louis Vuitton**: |
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- The visionary founder, **Louis Vuitton** (1821-1892) |
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- The luxury house, **Louis Vuitton** |
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- The giant luxury group **LVMH Moët Hennessy Louis Vuitton SE** |
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- The collection with Japanese artist, **Louis Vuitton x Yayoi Kusama** |
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|
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## . NER bespoke labels |
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|
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**Entities are evolving according to temporality, and context.** |
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|
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**Label** | **Description and example** |
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- | - |
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**O** | **Outside** (of a text segment) |
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**Date** | **Temporal expressions** (1854, Q2 2023, Nineties, September 21) |
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**Location** | **Physical location and area** (Paris, Japan, Europe, Champs-Elysées) |
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**Event** | **Critical events** (WW II, Olympics, IPO, Covid pandemic, Paris Fashion Week) |
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**MonetaryValue** | **Currency, price, sales, revenue** ($2.65 billion, 4.6 million euros, CHF 400,000, etc.) |
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**House** | **Fashion and luxury houses** (Louis Vuitton, Cartier, Gucci, Chanel) |
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**Brand** | **Sportswear, beauty and labels** (Nike, Lululemon, Clinique) |
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**FastFashion** | **Mass-market retailers** (Zara, H&M, Uniqlo, Shein) |
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**PrivateCompany** | **Unlisted companies** (Chanel SA, Stella McCartney Ltd, Valentino S.p.A) |
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**ListedGroup** | **Listed groups** (LVMH, Hermès International SCA, Kering) |
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**HoldingTrust** | **Holding and family office** (Agache, H51, Mousse Partners, Artèmis) |
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**InvestmentFirm** | **Investment banks, PE funds, M&A firms** (KKR, L Catterton, Mayhoola, Bernstein) |
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**MediaPublisher** | **Media outlets** (Bloomberg, Vogue, Business of Fashion, NYT) |
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**Hospitality** | **Luxury hospitality** (Ritz Paris, Belmond hotel Cipriani,Venetian Macao) |
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**MuseumGallery** | **Exhibition spaces** (Louvre, MET, Victoria & Albert, Pinault Collection) |
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**Retailer** | **POS, department stores, and select shops** (Bergdorf, Le Bon Marché, Takashimaya) |
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**Education** | **Business and fashion schools** (Polytechnic, Harvard, LSE, ESCP, Central Saint Martins, IFM) |
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**Organization** | **Legal, scientific, and cultural entities** (CFDA, European Union, UNESCO, SEC) |
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**ArtisticDirector** | **Lead creative of houses** (Karl Lagerfeld, Daniel Lee, Sarah Burton, Alessandro Michele) |
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**Executive** | **C-level, board members** (Jérôme Lambert, Sue Nabi, Pietro Beccari) |
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**Founder** | **Founder, creative, and owner** (Ralph Lauren, Rei Kawakubo, Michael Kors) |
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**Chairperson** | **Chairman/Chairwoman** (Bernard Arnault, Patrizio Bertelli, François-Henri Pinault) |
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**AnalystBanker** | **Equity analysts, M&A bankers** (Luca Solca, Pierre Mallevays, Louise Singlehurst) |
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**KOL** | **Artists, celebrities, historical figures** (Audrey Hepburn, BTS, Kanye West, Emma Watson) |
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**AthleteTeam** | **Professional athletes and teams** (David Beckham, Maria Sharapova, Luna Rossa, Scuderia Ferrari) |
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**Model** | **Fashion models** (Iman, Kate Moss, Adriana Lima, Naomi Campbell, Mariacarla Boscono) |
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**CreativeInsider** | **Photographers, make-up artists, watchmakers** (Steven Meisel, Dominique Ropion, Gérald Genta) |
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**EditorJournalist** | **Editor-in-chief, fashion editors, journalists** (Suzy Menkes, Anna Wintour, Carine Roitfeld) |
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**GarmCollection** | **Iconic garment and collections** (Haute Couture, Bar suit, No.13 of McQueen, Green Jungle Dress) |
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**Cosmetic** | **Cosmetic products** (Tilbury Glow palette, Crème de La Mer, YSL Nu, Viva Glam) |
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**Fragrance** | **Perfumes and EdT** (Chanel No.5, Dior Sauvage, Terre d'Hermès, Tom Ford Black Orchid) |
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**BagTrvlGoods** | **Bags, handbags, and leather goods** (Hermès Birkin bag, Louis Vuitton Speedy bag, Chanel 2.55) |
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**Jewelry** | **Fine jewellery, and gems** (Alhambra of Van Cleef & Arpels, Juste un Clou Cartier, The Winston Blue) |
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**Timepiece** | **Fine watches** (Nautilus Patek Philippe, Reverso Jaeger-Lecoultre, Rolex Oyster) |
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**Footwear** | **High heels to sneakers** (Rainbow of Ferragamo, Armadillo of McQueen, Air Force1) |
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**WineSpirit** | **Wine and spirit** (Château d'Yquem, Clos de Tart, Château Matras, Hennessy, Moet, Belvedere) |
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**Sustainability** | **Relevant ESG factors and entities** (Ethical Fashion Initiative, decoupling, biodiversity loss) |
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**CulturalArtifact** | **Songs, books, movies** (The Devil wears Prada, American Gigolo, Poker Face, The College Dropout) |
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|
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***Paper address and cite information: https://arxiv.org/abs/2409.15804*** |
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|
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### Citation info |
|
|
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``` |
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@misc{mousterou2024nerluxurynamedentityrecognition, |
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title={NER-Luxury: Named entity recognition for the fashion and luxury domain}, |
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author={Akim Mousterou}, |
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year={2024}, |
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eprint={2409.15804}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2409.15804}, |
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} |
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``` |
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|
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## How to use NER-Luxury with HuggingFace? |
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|
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#### Load NER-Luxury and its sub-word tokenizer : |
|
|
|
```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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|
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tokenizer = AutoTokenizer.from_pretrained("AkimfromParis/NER-Luxury") |
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model = AutoModelForTokenClassification.from_pretrained("AkimfromParis/NER-Luxury") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") |
|
|
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example = "CEO Leena Nair dismisses IPO rumours for Chanel." |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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|
|
# NER-Luxury |
|
|
|
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4079 |
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- Precision: 0.7652 |
|
- Recall: 0.8033 |
|
- F1: 0.7838 |
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- Accuracy: 0.9403 |
|
|
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## Model description |
|
|
|
More information needed |
|
|
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## Intended uses & limitations |
|
|
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More information needed |
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|
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## Training and evaluation data |
|
|
|
More information needed |
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|
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## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 8 |
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- mixed_precision_training: Native AMP |
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|
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### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 1.1269 | 1.0 | 1155 | 0.6237 | 0.6085 | 0.6716 | 0.6385 | 0.9005 | |
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| 0.5871 | 2.0 | 2310 | 0.4933 | 0.6857 | 0.7367 | 0.7103 | 0.9208 | |
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| 0.4517 | 3.0 | 3465 | 0.4470 | 0.7115 | 0.7639 | 0.7368 | 0.9273 | |
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| 0.3692 | 4.0 | 4620 | 0.4271 | 0.7298 | 0.7797 | 0.7539 | 0.9322 | |
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| 0.3121 | 5.0 | 5775 | 0.4103 | 0.7422 | 0.7906 | 0.7656 | 0.9362 | |
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| 0.2726 | 6.0 | 6930 | 0.4109 | 0.7531 | 0.7940 | 0.7730 | 0.9381 | |
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| 0.2138 | 7.0 | 8085 | 0.4088 | 0.7632 | 0.8005 | 0.7814 | 0.9397 | |
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| 0.1962 | 8.0 | 9240 | 0.4079 | 0.7652 | 0.8033 | 0.7838 | 0.9403 | |
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|
|
|
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### Framework versions |
|
|
|
- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
|
|
