| --- |
| tags: |
| - spacy |
| - token-classification |
| language: |
| - fr |
| model-index: |
| - name: fr_present_tense_value |
| results: |
| - task: |
| name: NER |
| type: token-classification |
| metrics: |
| - name: NER Precision |
| type: precision |
| value: 0.7757731959 |
| - name: NER Recall |
| type: recall |
| value: 0.7969991174 |
| - name: NER F Score |
| type: f_score |
| value: 0.7862429256 |
| |
| widget: |
| - text: "Le 2 décembre, c'est un vendredi, on avait un concert. On se retrouve avec des amis chez moi." |
| example_title: "present historique" |
| - text: "On danse toute la nuit et la vous vous dites qu c'est la meilleure manière de vivre." |
| example_title: "present génrique" |
| - text: "Je me souviens d'avoir vu un enfant danser sur le toît du monde !" |
| example_title: "présent ennonciation" |
| |
| license: agpl-3.0 |
| --- |
| |
| ## 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 |
|
|
| <details> |
|
|
| <summary>View label scheme (3 labels for 1 components)</summary> |
|
|
| | Component | Labels | |
| | --- | --- | |
| | **`ner`** | `PRESENT_ENNONCIATION`, `PRESENT_GENERIQUE`, `PRESENT_HISTORIQUE` | |
|
|
| </details> |
|
|
| ### 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| |
|
|
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|
