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--- |
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tags: |
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- spacy |
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- token-classification |
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language: |
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- fr |
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model-index: |
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- name: fr_on_value |
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results: |
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- task: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: NER Precision |
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type: precision |
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value: 0.8539823009 |
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- name: NER Recall |
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type: recall |
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value: 0.9234449761 |
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- name: NER F Score |
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type: f_score |
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value: 0.8873563218 |
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widget: |
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- text: "On m'a attrapé par la main !" |
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example_title: "on quelqu'un" |
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- text: "En France, on parle français." |
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example_title: "on générique" |
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- text: "On est allé manger des glaces puis on est allé à la plage." |
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example_title: "on nous" |
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license: agpl-3.0 |
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--- |
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## Description |
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This model was built to compute detect diffferent value of *on* in French (them). It's main purpose was to automate annotation on a specific dataset. |
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There is no waranty that it will work on any others dataset. |
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We finetune, the camembert-base model using this code; https://github.com/psycholinguistics2125/train_NER. |
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Some pronouns can have different meanings according to their context, the generic pronoun plays an important role in trauma narratives. |
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In our study, we differentiate the different values of the *on* pronoun. It can be used as *we*, for example: “On est entré au Bataclan à 20h45” ("We entered the Bataclan at 8:45 pm"). |
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But it can also be used as a synonym for someone: “On m’a marché dessus” (“Someone stepped on me"). |
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Finally, it can be used generically: “on est jamais mieux servi que par que par soi même” ("you are never better served than by yourself".) |
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--- |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `fr_on_value` | |
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| **Version** | `0.0.1` | |
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| **spaCy** | `>=3.4.4,<3.5.0` | |
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| **Default Pipeline** | `transformer`, `ner` | |
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| **Components** | `transformer`, `ner` | |
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | |
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| **Sources** | n/a | |
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| **License** | agpl-3.0 | |
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| **Author** | [n/a]() | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (3 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `ON_GENERIQUE`, `ON_NOUS`, `ON_QUELQU_UN` | |
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</details> |
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### Accuracy |
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| Type | Score | |
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| --- | --- | |
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| `ENTS_F` | 88.74 | |
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| `ENTS_P` | 85.40 | |
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| `ENTS_R` | 92.34 | |
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### training |
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We constructed our dataset by manually labeling the documents using Doccano, an open-source tool for collaborative human annotation. |
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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. |
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In order to ensure correct performance evaluation, the evaluation sequences were taken from documents that were not used during the training. |
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| label | train | test | valid | |
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| --- | --- |--- |--- | |
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| `ON_GENERIQUE`| 189 | 57 | 49 | |
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| `ON_NOUS`| 1006 | 320 | 229 | |
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| `ON_QUELQU_UN`|90 | 42 | 19| |
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