AntoineBourgois/propp-fr_NER_camembert-large_FAC_GPE_LOC_PER_TIME_VEH

language: fr tags:

• —NER
• —camembert
• —literary-texts
• —nested-entities
• —BookNLP-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, 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):

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:

Propp Documentation

TRAINING CORPUS:

PREDICTIONS CONFUSION MATRIX:

CONTACT:

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