Update README.md

README.md

@@ -20,7 +20,21 @@ model-index:
     - name: NER F Score
       type: f_score
       value: 0.8546448087
+
+widget:
+- text: "Il y avait du sang partout, less bras et less jambes n'étaient plus aux bons endroits."
+  example_title: "corps"
+- text: "J'était un peu dans le coton."
+  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` |
@@ -52,5 +66,17 @@ model-index:
 | `ENTS_F` | 85.46 |
 | `ENTS_P` | 85.37 |
 | `ENTS_R` | 85.56 |
-| `TRANSFORMER_LOSS` | 162304.44 |
-| `NER_LOSS` | 50389.76 |
+
+### 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 |