fr_sensations_and_body

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.

Label Scheme

<details> <summary>View label scheme (4 labels for 1 components)</summary> </details>

Accuracy

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.