metadata
tags:
- spacy
- token-classification
language:
- fr
model-index:
- name: fr_sensations_and_body
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8537117904
- name: NER Recall
type: recall
value: 0.8555798687
- name: NER F Score
type: f_score
value: 0.8546448087
widget:
- text: >-
Il y avait du sang partout, les bras et less jambes n'étaient plus aux
bons endroits.
example_title: corps
- text: J'étais un peu fatiguée.
example_title: Sensations physiques
- text: >-
Il y avait commme un silence assourdissant. Et là j'ai vu la beauté du
lévé de soleil.
example_title: Perceptions
This model was built to compute detect the lexical field of body, physical sensation and perception.
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.
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.