|
|
| --- |
| 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 |
|
|
