binbin83/fr_lexical_death

Description

This model was built to compute detect the lexical field of death. It's main purpose was to automate annotation on a specific dataset. There is no waranty that it will work on any others dataset. We finetune, the camembert-base model using this code; https://github.com/psycholinguistics2125/train_NER.

Feature	Description
Name	fr_lexical_death
Version	0.0.1
spaCy	>=3.4.4,<3.5.0
Default Pipeline	transformer, ner
Components	transformer, ner
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	n/a
License	agpl-3.0
Author	n/a

Label Scheme

View label scheme (2 labels for 1 components)

Component	Labels
ner	MORT_EXPLICITE, MORT_IMPLICITE

Accuracy

Type	Score
ENTS_F	77.61
ENTS_P	82.54
ENTS_R	73.24

Training

We constructed our dataset by manually labeling the documents using Doccano, an open-source tool for collaborative human annotation. The models were trained using 200-word length sequences, 70% of the data were used for the training, 20% to test and finetune hyperparameters, and 10% to evaluate the performances of the model. In order to ensure correct performance evaluation, the evaluation sequences were taken from documents that were not used during the training.

Tain dataset 147 labels for MORT_EXPLICITE

Test dataset is 35 labels for MORT_EXPLICITE

Valid dataset is 18 labels for MORT_EXPLICITE