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fr_on_value

Token Classification
spaCy
French
Eval Results
Model card Files Community
fr_on_value
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---
tags:
- spacy
- token-classification
language:
- fr
model-index:
- name: fr_on_value
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.8539823009
    - name: NER Recall
      type: recall
      value: 0.9234449761
    - name: NER F Score
      type: f_score
      value: 0.8873563218
widget:
- text: "On m'a attrapé par la main !"
  example_title: "on quelqu'un"
- text: "En France, on parle français."
  example_title: "on générique"
- text: "On est allé manger des glaces puis on est allé à la plage."
  example_title: "on nous"
  
license: agpl-3.0
---

## Description

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. 
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.
Some pronouns can have different meanings according to their context, the generic pronoun plays an important role in trauma narratives.
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"). 
But it can also be used as a synonym for someone: “On m’a marché dessus” (“Someone stepped on me"). 
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".)

---
| Feature | Description |
| --- | --- |
| **Name** | `fr_on_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`** | `ON_GENERIQUE`, `ON_NOUS`, `ON_QUELQU_UN` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `ENTS_F` | 88.74 |
| `ENTS_P` | 85.40 |
| `ENTS_R` | 92.34 |


### 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 |
| --- | --- |--- |--- |
| `ON_GENERIQUE`| 189 | 57 | 49 | 
| `ON_NOUS`| 1006 | 320 | 229 | 
| `ON_QUELQU_UN`|90 | 42 | 19|