--- 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 : ```python 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](https://huggingface.co/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