Description
This model was built to compute detect diffferent value of present tense in French (them). 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.
Tthe present tense might have different meanings depending on the context. It can have a historical value, referring to the past, and it also makes the speech more alive.
Another meaning is generic, to express general truths like definitions or properties. Finally, it can have an enunciation value by referring to the present moment, to describe an ongoing action.
These different values of the present tense can only be differentiated by the context.
This is the reason why models based on contextual embedding (BERT like) should be relevant to differentiate them.
| Feature |
Description |
| Name |
fr_present_tense_value |
| 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 (3 labels for 1 components)
| Component |
Labels |
ner |
PRESENT_ENNONCIATION, PRESENT_GENERIQUE, PRESENT_HISTORIQUE |
Accuracy
| Type |
Score |
ENTS_F |
78.62 |
ENTS_P |
77.58 |
ENTS_R |
79.70 |
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.
| label |
train |
test |
valid |
PRESENT_ENNONCIATION |
2069 |
673 |
438 |
PRESENT_GENERIQUE |
704 |
177 |
147 |
PRESENT_HISTORIQUE |
1005 |
289 |
285 |
