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binbin83/fr_lexical_death
fr_lexical_death is a token classification model from binbin83. Use it when you need labels on individual words, such as names. It is set up for spacy. The card lists the license as agpl-3.0.
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 came…
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From the Hugging Face model README
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 |
| Component | Labels |
|---|---|
ner | MORT_EXPLICITE, MORT_IMPLICITE |
| Type | Score |
|---|---|
ENTS_F | 77.61 |
ENTS_P | 82.54 |
ENTS_R | 73.24 |
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