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---
language: fr
tags:
- NER
- camembert
- literary-texts
- nested-entities
- propp-fr
license: apache-2.0
metrics:
- f1
- precision
- recall
base_model:
- almanach/camembert-large
pipeline_tag: token-classification
---
## INTRODUCTION:
This model, developed as part of the [propp-fr project](https://lattice-8094.github.io/propp/), is a **NER model** built on top of [camembert-large](https://huggingface.co/almanach/camembert-large) embeddings, trained to predict nested entities in french, specifically for literary texts.
The predicted entities are:
- mentions of characters (PER): pronouns (je, tu, il, ...), possessive pronouns (mon, ton, son, ...), common nouns (le capitaine, la princesse, ...) and proper nouns (Indiana Delmare, Honoré de Pardaillan, ...)
- facilities (FAC): chatêau, sentier, chambre, couloir, ...
- time (TIME): le règne de Louis XIV, ce matin, en juillet, ...
- geo-political entities (GPE): Montrouge, France, le petit hameau, ...
- locations (LOC): le sud, Mars, l'océan, le bois, ...
- vehicles (VEH): avion, voitures, calèche, vélos, ...
## MODEL PERFORMANCES (LOOCV):
| NER_tag | precision | recall | f1_score | support | support % |
|-----------|-------------|----------|------------|-----------|-------------|
| PER | 94.58% | 95.16% | 94.87% | 71,738 | 100.00% |
| micro_avg | 94.58% | 95.16% | 94.87% | 71,738 | 100.00% |
| macro_avg | 94.58% | 95.16% | 94.87% | 71,738 | 100.00% |
## TRAINING PARAMETERS:
- Entities types: ['PER']
- Tagging scheme: BIOES
- Nested entities levels: [0, 1]
- Split strategy: Leave-one-out cross-validation (31 files)
- Train/Validation split: 0.85 / 0.15
- Batch size: 16
- Initial learning rate: 0.00014
## MODEL ARCHITECTURE:
Model Input: Maximum context camembert-large embeddings (1024 dimensions)
- Locked Dropout: 0.5
- Projection layer:
- layer type: highway layer
- input: 1024 dimensions
- output: 2048 dimensions
- BiLSTM layer:
- input: 2048 dimensions
- output: 256 dimensions (hidden state)
- Linear layer:
- input: 256 dimensions
- output: 5 dimensions (predicted labels with BIOES tagging scheme)
- CRF layer
Model Output: BIOES labels sequence
## HOW TO USE:
[Propp Documentation](https://lattice-8094.github.io/propp/quick_start/)
## TRAINING CORPUS:
| | Document | Tokens Count | Is included in model eval |
|----|---------------------------------------------------------------------------------|----------------|-----------------------------------|
| 0 | 1731_Prévost-Antoine-François_Manon-Lescaut_PER-ONLY | 71,219 tokens | True |
| 1 | 1830_Balzac-Honoré-de_La-maison-du-chat-qui-pelote | 24,776 tokens | True |
| 2 | 1830_Balzac-Honoré-de_Sarrasine | 15,408 tokens | True |
| 3 | 1832_Sand-George_Indiana_PER-ONLY | 112,221 tokens | True |
| 4 | 1836_Gautier-Théophile_La-morte-amoureuse | 14,293 tokens | True |
| 5 | 1837_Balzac-Honoré-de_La-maison-Nucingen | 30,030 tokens | True |
| 6 | 1841_Sand-George_Pauline | 12,398 tokens | True |
| 7 | 1856_Cousin-Victor_Madame-de-Hautefort | 11,768 tokens | True |
| 8 | 1863_Gautier-Théophile_Le-capitaine-Fracasse | 11,848 tokens | True |
| 9 | 1873_Zola-Émile_Le-ventre-de-Paris | 12,613 tokens | True |
| 10 | 1881_Flaubert-Gustave_Bouvard-et-Pécuchet | 12,308 tokens | True |
| 11 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-La-buche | 2,267 tokens | True |
| 12 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-La-relique | 2,041 tokens | True |
| 13 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-La-rouille | 2,949 tokens | True |
| 14 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Madame-Baptiste | 2,578 tokens | True |
| 15 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Marocca | 4,078 tokens | True |
| 16 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-A-cheval | 2,878 tokens | True |
| 17 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-Fou | 1,905 tokens | True |
| 18 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-Mademoiselle-Fifi | 5,439 tokens | True |
| 19 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-Reveil | 2,159 tokens | True |
| 20 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-Un-reveillon | 2,364 tokens | True |
| 21 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-Une-ruse | 2,469 tokens | True |
| 22 | 1901_Achard-Lucie_Rosalie-de-Constant-sa-famille-et-ses-amis | 12,775 tokens | True |
| 23 | 1903_Conan-Laure_Élisabeth-Seton | 13,046 tokens | True |
| 24 | 1904-1912_Rolland-Romain_Jean-Christophe(1) | 10,982 tokens | True |
| 25 | 1904-1912_Rolland-Romain_Jean-Christophe(2) | 10,305 tokens | True |
| 26 | 1917_Bourgeois-Adèle_Némoville | 12,468 tokens | True |
| 27 | 1923_Delly_Dans-les-ruines | 95,617 tokens | True |
| 28 | 1923_Radiguet-Raymond_Le-diable-au-corps | 14,850 tokens | True |
| 29 | 1926_Audoux-Marguerite_De-la-ville-au-moulin | 12,144 tokens | True |
| 30 | 1937_Audoux-Marguerite_Douce-Lumière | 12,346 tokens | True |
| 31 | TOTAL | 554,542 tokens | 3 files used for cross-validation |
## PREDICTIONS CONFUSION MATRIX:
| Gold Labels | PER | O | support |
|---------------|--------|-------|-----------|
| PER | 68,267 | 3,471 | 71,738 |
| O | 3,910 | 0 | 3,910 |
## CONTACT:
mail: antoine [dot] bourgois [at] protonmail [dot] com
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