Update README.md

README.md

@@ -20,7 +20,19 @@ model-index:
     - name: NER F Score
       type: f_score
       value: 0.776119403
+license: agpl-3.0
+widget:
+- example 1: "Il faut pas sortir, vous reviendrez pas vivantes."
+- example 2: "Les morts ne parlents pas."
+- example 3: "Les Ambulances garés, les cortèges de defunts, les cadavres qu'on sortait des décombres"
 ---
+
+## Description
+
+This model was built to compute detect the lexical field of death. 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_lexical_death` |
@@ -53,4 +65,18 @@ model-index:
 | `ENTS_P` | 82.54 |
 | `ENTS_R` | 73.24 |
 | `TRANSFORMER_LOSS` | 51778.17 |
-| `NER_LOSS` | 41163.78 |
+| `NER_LOSS` | 41163.78 |
+
+###  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.
+
+Tain dataset 147 labels for MORT_EXPLICITE
+
+Test  dataset is 35 labels for  MORT_EXPLICITE
+
+Valid dataset is 18 labels  for MORT_EXPLICITE
+