INTRODUCTION:

This model, developed as part of the BookNLP-fr project, is a NER model built on top of 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 90.58% 93.52% 92.03% 31,570 83.87%
FAC 70.49% 71.75% 71.12% 2,294 6.09%
TIME 58.40% 58.68% 58.54% 1,670 4.44%
GPE 76.69% 74.05% 75.35% 871 2.31%
LOC 60.92% 44.37% 51.35% 773 2.05%
VEH 66.18% 49.25% 56.47% 465 1.24%
micro_avg 86.70% 88.64% 87.61% 37,643 100.00%
macro_avg 70.55% 65.27% 67.48% 37,643 100.00%

TRAINING PARAMETERS:

  • Entities types: ['PER', 'LOC', 'FAC', 'TIME', 'VEH', 'GPE']
  • Tagging scheme: BIOES
  • Nested entities levels: [0, 1]
  • Split strategy: Leave-one-out cross-validation (28 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: 25 dimensions (predicted labels with BIOES tagging scheme)
  • CRF layer

Model Output: BIOES labels sequence

HOW TO USE:

*** IN CONSTRUCTION ***

TRAINING CORPUS:

Document Tokens Count Is included in model eval
0 1836_Gautier-Theophile_La-morte-amoureuse 14,299 tokens True
1 1840_Sand-George_Pauline 12,315 tokens True
2 1842_Balzac-Honore-de_La-Maison-du-chat-qui-pelote 24,776 tokens True
3 1844_Balzac-Honore-de_La-Maison-Nucingen 30,987 tokens True
4 1844_Balzac-Honore-de_Sarrasine 15,408 tokens True
5 1856_Cousin-Victor_Madame-de-Hautefort 11,768 tokens True
6 1863_Gautier-Theophile_Le-capitaine-Fracasse 11,834 tokens True
7 1873_Zola-Emile_Le-ventre-de-Paris 12,557 tokens True
8 1881_Flaubert-Gustave_Bouvard-et-Pecuchet 12,281 tokens True
9 1882_Guy-de-Maupassant_Mademoiselle-Fifi-1_1-MADEMOISELLE-FIFI 5,425 tokens True
10 1882_Guy-de-Maupassant_Mademoiselle-Fifi-1_2-MADAME-BAPTISTE 2,554 tokens True
11 1882_Guy-de-Maupassant_Mademoiselle-Fifi-1_3-LA-ROUILLE 2,929 tokens True
12 1882_Guy-de-Maupassant_Mademoiselle-Fifi-2_1-MARROCA 4,067 tokens True
13 1882_Guy-de-Maupassant_Mademoiselle-Fifi-2_2-LA-BUCHE 2,251 tokens True
14 1882_Guy-de-Maupassant_Mademoiselle-Fifi-2_3-LA-RELIQUE 2,034 tokens True
15 1882_Guy-de-Maupassant_Mademoiselle-Fifi-3_1-FOU 1,864 tokens True
16 1882_Guy-de-Maupassant_Mademoiselle-Fifi-3_2-REVEIL 2,141 tokens True
17 1882_Guy-de-Maupassant_Mademoiselle-Fifi-3_3-UNE-RUSE 2,441 tokens True
18 1882_Guy-de-Maupassant_Mademoiselle-Fifi-3_4-A-CHEVAL 2,860 tokens True
19 1882_Guy-de-Maupassant_Mademoiselle-Fifi-3_5-UN-REVEILLON 2,343 tokens True
20 1901_Lucie-Achard_Rosalie-de-Constant-sa-famille-et-ses-amis 12,703 tokens True
21 1903_Conan-Laure_Elisabeth_Seton 13,023 tokens True
22 1904_Rolland-Romain_Jean-Christophe_Tome-I-L-aube 10,982 tokens True
23 1904_Rolland-Romain_Jean-Christophe_Tome-II-Le-matin 10,305 tokens True
24 1917_Adèle-Bourgeois_Némoville 12,389 tokens True
25 1923_Radiguet-Raymond_Le-diable-au-corps 14,637 tokens True
26 1926_Audoux-Marguerite_De-la-ville-au-moulin 11,902 tokens True
27 1937_Audoux-Marguerite_Douce-Lumiere 12,285 tokens True
28 TOTAL 275,360 tokens 28 files used for cross-validation

PREDICTIONS CONFUSION MATRIX:

Gold Labels PER FAC TIME GPE LOC VEH O support
PER 29,525 27 13 6 7 26 1,966 31,570
FAC 43 1,646 0 17 12 2 574 2,294
TIME 5 1 980 1 1 0 682 1,670
GPE 18 28 1 645 27 0 152 871
LOC 5 63 0 54 343 0 308 773
VEH 58 8 1 0 0 229 169 465
O 2,902 532 682 110 167 89 0 4,482

CONTACT:

mail: antoine [dot] bourgois [at] protonmail [dot] com

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