transformers safetensors xlm-roberta token-classification NER Fashion Luxury en arxiv:2409.15804 base_model:FacebookAI/xlm-roberta-base base_model:finetune:FacebookAI/xlm-roberta-base license:bigscience-openrail-m model-index autotrain_compatible endpoints_compatible region:us
---
base_model: xlm-roberta-base
language: en
library_name: transformers
license: bigscience-openrail-m
pipeline_tag: token-classification
tags:
- NER
- token-classification
- Fashion
- Luxury
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
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
model-index:
- name: AkimfromParis/Bert-Luxury
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: Private
      type: private
    metrics:
    - type: Loss
      value: 0.4079
      name: Loss
      verified: true
    - type: Precision
      value: 0.7652
      name: Precision
      verified: true
    - type: Recall
      value: 0.8033
      name: Recall
      verified: true
    - type: F1
      value: 0.7838
      name: F1
      verified: true
    - type: Accuracy
      value: 0.9403
      name: Accuracy
      verified: true
---

# ***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
