---
tags:
- spacy
- token-classification
language:
- fr
model-index:
- name: fr_sensations_and_body
  results:
  - task:
      name: NER
      type: token-classification
    metrics:
    - name: NER Precision
      type: precision
      value: 0.8537117904
    - name: NER Recall
      type: recall
      value: 0.8555798687
    - name: NER F Score
      type: f_score
      value: 0.8546448087

widget:
- text: "Il y avait du sang partout, les bras et less jambes n'étaient plus aux bons endroits."
  example_title: "corps"
- text: "J'étais un peu fatiguée."
  example_title: "Sensations physiques"
- text: "Il y avait commme un silence assourdissant. Et là j'ai vu la beauté du lévé de soleil."
  example_title: "Perceptions"
---

This model was built to compute detect the lexical field of body, physical sensation and perception.
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_sensations_and_body` |
| **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** | n/a |
| **Author** | [n/a]() |

### Label Scheme

<details>

<summary>View label scheme (4 labels for 1 components)</summary>

| Component | Labels |
| --- | --- |
| **`ner`** | `CORPS`, `MOTS_PERCEPTIONS_SENSORIELLES`, `SENSATIONS_PHYSIQUES`, `VERB_PERCEPTIONS_SENSORIELLES` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `ENTS_F` | 85.46 |
| `ENTS_P` | 85.37 |
| `ENTS_R` | 85.56 |

### 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 |
| --- | --- |--- |--- |
| `CORPS`| 523 | 152 | 106 | 
| `MOTS_PERCEPTIONS_SENSORIELLES`| 250 | 108 | 82 | 
| `SENSATIONS_PHYSIQUES`|91 | 38 | 31| 
| `VERB_PERCEPTIONS_SENSORIELLES` |617|162 | 137 |