Update README.md

README.md

@@ -1,41 +1,969 @@
 ---
+license: mit
 base_model: camembert/camembert-large
-tags:
-- generated_from_trainer
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: camembert-large-frenchNER_4entities
+- name: NERmembert-large-4entities
   results: []
+datasets:
+- CATIE-AQ/frenchNER_4entities
+language:
+- fr
+widget:
+- text: "Assurés de disputer l'Euro 2024 en Allemagne l'été prochain (du 14 juin au 14 juillet) depuis leur victoire aux Pays-Bas, les Bleus ont fait le nécessaire pour avoir des certitudes. Avec six victoires en six matchs officiels et un seul but encaissé, Didier Deschamps a consolidé les acquis de la dernière Coupe du monde. Les joueurs clés sont connus : Kylian Mbappé, Aurélien Tchouameni, Antoine Griezmann, Ibrahima Konaté ou encore Mike Maignan."
+library_name: transformers
+pipeline_tag: token-classification
+co2_eq_emissions: 80
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
-# camembert-large-frenchNER_4entities
+# NERmembert-large-4entities
 
-This model is a fine-tuned version of [camembert/camembert-large](https://huggingface.co/camembert/camembert-large) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0532
-- Precision: 0.9860
-- Recall: 0.9860
-- F1: 0.9860
-- Accuracy: 0.9860
+## Model Description
 
-## Model description
+We present **NERmembert-large-4entities**, which is a [CamemBERT large](https://huggingface.co/camembert/camembert-large) fine-tuned for the Name Entity Recognition task for the French language on four French NER datasets for 4 entities (LOC, PER, ORG, MISC).  
+All these datasets were concatenated and cleaned into a single dataset that we called [frenchNER_4entities](https://huggingface.co/datasets/CATIE-AQ/frenchNER_4entities).  
+There are a total of **384,773** rows, of which **328,757** are for training, **24,131** for validation and **31,885** for testing.  
+Our methodology is described in a blog post available in [English](https://blog.vaniila.ai/en/NER_en/) or [French](https://blog.vaniila.ai/NER/).
 
-More information needed
 
-## Intended uses & limitations
 
-More information needed
+## Dataset
 
-## Training and evaluation data
+The dataset used is [frenchNER_4entities](https://huggingface.co/datasets/CATIE-AQ/frenchNER_4entities), which represents ~385k sentences labeled in 4 categories:
+| Label | Examples                                                   |
+|:------|:-----------------------------------------------------------|
+| PER   | "La Bruyère", "Gaspard de Coligny", "Wittgenstein"         |
+| ORG   | "UTBM", "American Airlines", "id Software"                 |
+| LOC   | "République du Cap-Vert", "Créteil", "Bordeaux"            |
+| MISC  | "Wolfenstein 3D", "Révolution française", "Coupe du monde" |
+
+The distribution of the entities is as follows:
+
+<table>
+<thead>
+  <tr>
+    <th><br>Splits</th>
+    <th><br>O</th>
+    <th><br>PER</th>
+    <th><br>LOC</th>  
+    <th><br>ORG</th>
+    <th><br>MISC</th>
+  </tr>
+</thead>
+<tbody>
+    <td><br>train</td>
+    <td><br>7,539,692</td>
+    <td><br>307,144</td>
+    <td><br>286,746</td>
+    <td><br>127,089</td>
+    <td><br>799,494</td>  
+  </tr>
+  <tr>
+    <td><br>validation</td>
+    <td><br>544,580</td>
+    <td><br>24,034</td>
+    <td><br>21,585</td>
+    <td><br>5,927</td>
+    <td><br>18,221</td>  
+  </tr>
+  <tr>
+    <td><br>test</td>
+    <td><br>720,623</td>
+    <td><br>32,870</td>
+    <td><br>29,683</td>
+    <td><br>7,911</td>
+    <td><br>21,760</td>  
+  </tr>
+</tbody>
+</table>
+
+
+## Evaluation results
+
+The evaluation was carried out using the [**evaluate**](https://pypi.org/project/evaluate/) python package.
+
+### frenchNER_4entities
+
+For space reasons, we show only the F1 of the different models. You can see the full results below the table.
+
+<table>
+<thead>
+    <tr>
+      <th><br>Model</th>
+      <th><br>PER</th>
+      <th><br>LOC</th>
+      <th><br>ORG</th>
+      <th><br>MISC</th>
+    </tr>
+</thead>
+<tbody>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td>
+        <td><br>0.971</td>
+        <td><br>0.947</td>
+        <td><br>0.902</td>
+        <td><br>0.663</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td>
+        <td><br>0.974</td>
+        <td><br>0.948</td>
+        <td><br>0.892</td>
+        <td><br>0.658</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
+        <td><br>0.978</td>
+        <td><br>0.957</td>
+        <td><br>0.904</td>
+        <td><br>0</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-base-4entities">NERmembert-base-4entities</a></td>
+        <td><br>0.978</td>
+        <td><br>0.958</td>
+        <td><br>0.903</td>
+        <td><br>0.814</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br>NERmembert-large-4entities (this model)</td>
+        <td><br>0.982</td>
+        <td><br>0.964</td>
+        <td><br>0.919</td>
+        <td><br>0.834</td>
+    </tr>
+</tbody>
+</table>
+
+
+<details>
+<summary>Full results</summary>
+<table>
+<thead>
+    <tr>
+      <th><br>Model</th>
+      <th><br>Metrics</th>
+      <th><br>PER</th>
+      <th><br>LOC</th>
+      <th><br>ORG</th>
+      <th><br>MISC</th>
+      <th><br>O</th>
+      <th><br>Overall</th>
+    </tr>
+</thead>
+<tbody>
+    <tr>
+        <td rowspan="3"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td>
+        <td><br>Precision</td>
+        <td><br>0.952</td>
+        <td><br>0.924</td>
+        <td><br>0.870</td>
+        <td><br>0.845</td>
+        <td><br>0.986</td>
+        <td><br>0.976</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.990</td>
+        <td><br>0.972</td>
+        <td><br>0.938</td>
+        <td><br>0.546</td>
+        <td><br>0.992</td>
+        <td><br>0.976</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.971</td>
+        <td><br>0.947</td>
+        <td><br>0.902</td>
+        <td><br>0.663</td>
+        <td><br>0.989</td>
+        <td><br>0.976</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td>
+        <td><br>Precision</td>
+        <td><br>0.962</td>
+        <td><br>0.933</td>
+        <td><br>0.857</td>
+        <td><br>0.830</td>
+        <td><br>0.985</td>
+        <td><br>0.976</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.987</td>
+        <td><br>0.963</td>
+        <td><br>0.930</td>
+        <td><br>0.545</td>
+        <td><br>0.993</td>
+        <td><br>0.976</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.974</td>
+        <td><br>0.948</td>
+        <td><br>0.892</td>
+        <td><br>0.658</td>
+        <td><br>0.989</td>
+        <td><br>0.976</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
+        <td><br>Precision</td>
+        <td><br>0.973</td>
+        <td><br>0.955</td>
+        <td><br>0.886</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.983</td>
+        <td><br>0.960</td>
+        <td><br>0.923</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.978</td>
+        <td><br>0.957</td>
+        <td><br>0.904</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-4entities">NERmembert-base-4entities</a></td>
+        <td><br>Precision</td>
+        <td><br>0.973</td>
+        <td><br>0.951</td>
+        <td><br>0.888</td>
+        <td><br>0.850</td>
+        <td><br>0.993</td>
+        <td><br>0.984</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.983</td>
+        <td><br>0.964</td>
+        <td><br>0.918</td>
+        <td><br>0.781</td>
+        <td><br>0.993</td>
+        <td><br>0.984</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.978</td>
+        <td><br>0.958</td>
+        <td><br>0.903</td>
+        <td><br>0.814</td>
+        <td><br>0.993</td>
+        <td><br>0.984</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br>NERmembert-large-4entities (this model)</td>
+        <td><br>Precision</td>
+        <td><br>0.977</td>
+        <td><br>0.961</td>
+        <td><br>0.896</td>
+        <td><br>0.872</td>
+        <td><br>0.993</td>
+        <td><br>0.986</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.987</td>
+        <td><br>0.966</td>
+        <td><br>0.943</td>
+        <td><br>0.798</td>
+        <td><br>0.995</td>
+        <td><br>0.986</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.982</td>
+        <td><br>0.964</td>
+        <td><br>0.919</td>
+        <td><br>0.834</td>
+        <td><br>0.994</td>
+        <td><br>0.986</td>
+    </tr>
+</tbody>
+</table>
+</details>
+
+In detail:
+
+### multiconer
+
+For space reasons, we show only the F1 of the different models. You can see the full results below the table.
+
+<table>
+<thead>
+    <tr>
+      <th><br>Model</th>
+      <th><br>PER</th>
+      <th><br>LOC</th>
+      <th><br>ORG</th>
+      <th><br>MISC</th>
+    </tr>
+</thead>
+<tbody>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td>
+        <td><br>0.940</td>
+        <td><br>0.761</td>
+        <td><br>0.723</td>
+        <td><br>0.560</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td>
+        <td><br>0.921</td>
+        <td><br>0.748</td>
+        <td><br>0.694</td>
+        <td><br>0.530</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
+        <td><br>0.960</td>
+        <td><br>0.887</td>
+        <td><br>0.877</td>
+        <td><br>0</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-base-4entities">NERmembert-base-4entities</a></td>
+        <td><br>0.960</td>
+        <td><br>0.890</td>
+        <td><br>0.867</td>
+        <td><br>0.852</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br>NERmembert-large-4entities (this model)</td>
+        <td><br>0.969</td>
+        <td><br>0.919</td>
+        <td><br>0.904</td>
+        <td><br>0.864</td>
+    </tr>
+</tbody>
+</table>
+
+<details>
+<summary>Full results</summary>
+<table>
+<thead>
+    <tr>
+      <th><br>Model</th>
+      <th><br>Metrics</th>
+      <th><br>PER</th>
+      <th><br>LOC</th>
+      <th><br>ORG</th>
+      <th><br>MISC</th>
+      <th><br>O</th>
+      <th><br>Overall</th>
+    </tr>
+</thead>
+<tbody>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td>
+        <td><br>Precision</td>
+        <td><br>0.908</td>
+        <td><br>0.717</td>
+        <td><br>0.753</td>
+        <td><br>0.620</td>
+        <td><br>0.936</td>
+        <td><br>0.889</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.975</td>
+        <td><br>0.811</td>
+        <td><br>0.696</td>
+        <td><br>0.511</td>
+        <td><br>0.938</td>
+        <td><br>0.889</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.940</td>
+        <td><br>0.761</td>
+        <td><br>0.723</td>
+        <td><br>0.560</td>
+        <td><br>0.937</td>
+        <td><br>0.889</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td>
+        <td><br>Precision</td>
+        <td><br>0.885</td>
+        <td><br>0.738</td>
+        <td><br>0.737</td>
+        <td><br>0.589</td>
+        <td><br>0.928</td>
+        <td><br>0.881</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.960</td>
+        <td><br>0.759</td>
+        <td><br>0.655</td>
+        <td><br>0.482</td>
+        <td><br>0.939</td>
+        <td><br>0.881</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.921</td>
+        <td><br>0.748</td>
+        <td><br>0.694</td>
+        <td><br>0.530</td>
+        <td><br>0.934</td>
+        <td><br>0.881</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
+        <td><br>Precision</td>
+        <td><br>0.957</td>
+        <td><br>0.894</td>
+        <td><br>0.876</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.962</td>
+        <td><br>0.880</td>
+        <td><br>0.878</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.960</td>
+        <td><br>0.887</td>
+        <td><br>0.877</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-4entities">NERmembert-base-4entities</a></td>
+        <td><br>Precision</td>
+        <td><br>0.954</td>
+        <td><br>0.893</td>
+        <td><br>0.851</td>
+        <td><br>0.849</td>
+        <td><br>0.979</td>
+        <td><br>0.954</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.967</td>
+        <td><br>0.887</td>
+        <td><br>0.883</td>
+        <td><br>0.855</td>
+        <td><br>0.974</td>
+        <td><br>0.954</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.960</td>
+        <td><br>0.890</td>
+        <td><br>0.867</td>
+        <td><br>0.852</td>
+        <td><br>0.977</td>
+        <td><br>0.954</td>
+    </tr>
+        <tr>
+        <td rowspan="3"><br>NERmembert-large-4entities (this model)</td>
+        <td><br>Precision</td>
+        <td><br>0.964</td>
+        <td><br>0.922</td>
+        <td><br>0.904</td>
+        <td><br>0.856</td>
+        <td><br>0.981</td>
+        <td><br>0.961</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.975</td>
+        <td><br>0.917</td>
+        <td><br>0.904</td>
+        <td><br>0.872</td>
+        <td><br>0.976</td>
+        <td><br>0.961</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.969</td>
+        <td><br>0.919</td>
+        <td><br>0.904</td>
+        <td><br>0.864</td>
+        <td><br>0.978</td>
+        <td><br>0.961</td>
+    </tr>
+</tbody>
+</table>
+</details>
+
+
+### multinerd
+
+For space reasons, we show only the F1 of the different models. You can see the full results below the table.
+
+<table>
+<thead>
+    <tr>
+      <th><br>Model</th>
+      <th><br>PER</th>
+      <th><br>LOC</th>
+      <th><br>ORG</th>
+      <th><br>MISC</th>
+    </tr>
+</thead>
+<tbody>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td>
+        <td><br>0.962</td>
+        <td><br>0.934</td>
+        <td><br>0.888</td>
+        <td><br>0.419</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td>
+        <td><br>0.972</td>
+        <td><br>0.938</td>
+        <td><br>0.884</td>
+        <td><br>0.430</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
+        <td><br>0.985</td>
+        <td><br>0.973</td>
+        <td><br>0.938</td>
+        <td><br>0</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-base-4entities">NERmembert-base-4entities</a></td>
+        <td><br>0.985</td>
+        <td><br>0.973</td>
+        <td><br>0.938</td>
+        <td><br>0.770</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br>NERmembert-large-4entities (this model)</td>
+        <td><br>0.987</td>
+        <td><br>0.976</td>
+        <td><br>0.948</td>
+        <td><br>0.790</td>
+    </tr>
+</tbody>
+</table>
+
+<details>
+<summary>Full results</summary>
+<table>
+<thead>
+    <tr>
+      <th><br>Model</th>
+      <th><br>Metrics</th>
+      <th><br>PER</th>
+      <th><br>LOC</th>
+      <th><br>ORG</th>
+      <th><br>MISC</th>
+      <th><br>O</th>
+      <th><br>Overall</th>
+    </tr>
+</thead>
+<tbody>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td>
+        <td><br>Precision</td>
+        <td><br>0.931</td>
+        <td><br>0.893</td>
+        <td><br>0.827</td>
+        <td><br>0.725</td>
+        <td><br>0.979</td>
+        <td><br>0.966</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.994</td>
+        <td><br>0.980</td>
+        <td><br>0.959</td>
+        <td><br>0.295</td>
+        <td><br>0.990</td>
+        <td><br>0.966</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.962</td>
+        <td><br>0.934</td>
+        <td><br>0.888</td>
+        <td><br>0.419</td>
+        <td><br>0.984</td>
+        <td><br>0.966</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td>
+        <td><br>Precision</td>
+        <td><br>0.954</td>
+        <td><br>0.908</td>
+        <td><br>0.817</td>
+        <td><br>0.705</td>
+        <td><br>0.977</td>
+        <td><br>0.967</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.991</td>
+        <td><br>0.969</td>
+        <td><br>0.963</td>
+        <td><br>0.310</td>
+        <td><br>0.990</td>
+        <td><br>0.967</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.972</td>
+        <td><br>0.938</td>
+        <td><br>0.884</td>
+        <td><br>0.430</td>
+        <td><br>0.984</td>
+        <td><br>0.967</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
+        <td><br>Precision</td>
+        <td><br>0.974</td>
+        <td><br>0.965</td>
+        <td><br>0.910</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.995</td>
+        <td><br>0.981</td>
+        <td><br>0.968</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.985</td>
+        <td><br>0.973</td>
+        <td><br>0.938</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+      <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-4entities">NERmembert-base-4entities</a></td>
+      <td><br>Precision</td>
+        <td><br>0.976</td>
+        <td><br>0.961</td>
+        <td><br>0.91</td>
+        <td><br>0.829</td>
+        <td><br>0.991</td>
+        <td><br>0.983</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.994</td>
+        <td><br>0.985</td>
+        <td><br>0.967</td>
+        <td><br>0.719</td>
+        <td><br>0.993</td>
+        <td><br>0.983</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.985</td>
+        <td><br>0.973</td>
+        <td><br>0.938</td>
+        <td><br>0.770</td>
+        <td><br>0.992</td>
+        <td><br>0.983</td>
+    </tr>
+        <tr>
+        <td rowspan="3"><br>NERmembert-large-4entities (this model)</td>
+        <td><br>Precision</td>
+        <td><br>0.979</td>
+        <td><br>0.967</td>
+        <td><br>0.922</td>
+        <td><br>0.852</td>
+        <td><br>0.991</td>
+        <td><br>0.985</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.996</td>
+        <td><br>0.986</td>
+        <td><br>0.974</td>
+        <td><br>0.736</td>
+        <td><br>0.994</td>
+        <td><br>0.985</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.987</td>
+        <td><br>0.976</td>
+        <td><br>0.948</td>
+        <td><br>0.790</td>
+        <td><br>0.993</td>
+        <td><br>0.985</td>
+    </tr>
+</tbody>
+</table>
+</details>
+
+### wikiner
+
+For space reasons, we show only the F1 of the different models. You can see the full results below the table.
+
+<table>
+<thead>
+    <tr>
+      <th><br>Model</th>
+      <th><br>PER</th>
+      <th><br>LOC</th>
+      <th><br>ORG</th>
+      <th><br>MISC</th>
+    </tr>
+</thead>
+<tbody>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td>
+        <td><br>0.986</td>
+        <td><br>0.966</td>
+        <td><br>0.938</td>
+        <td><br>0.938</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td>
+        <td><br>0.983</td>
+        <td><br>0.964</td>
+        <td><br>0.925</td>
+        <td><br>0.926</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
+        <td><br>0.970</td>
+        <td><br>0.945</td>
+        <td><br>0.878</td>
+        <td><br>0</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-base-4entities">NERmembert-base-4entities</a></td>
+        <td><br>0.970</td>
+        <td><br>0.945</td>
+        <td><br>0.876</td>
+        <td><br>0.872</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br>NERmembert-large-4entities (this model)</td>
+        <td><br>0.975</td>
+        <td><br>0.953</td>
+        <td><br>0.896</td>
+        <td><br>0.893</td>
+    </tr>
+</tbody>
+</table>
+
+<details>
+<summary>Full results</summary>
+<table>
+<thead>
+    <tr>
+      <th><br>Model</th>
+      <th><br>Metrics</th>
+      <th><br>PER</th>
+      <th><br>LOC</th>
+      <th><br>ORG</th>
+      <th><br>MISC</th>
+      <th><br>O</th>
+      <th><br>Overall</th>
+    </tr>
+</thead>
+<tbody>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td>
+        <td><br>Precision</td>
+        <td><br>0.986</td>
+        <td><br>0.962</td>
+        <td><br>0.925</td>
+        <td><br>0.943</td>
+        <td><br>0.998</td>
+        <td><br>0.992</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.987</td>
+        <td><br>0.969</td>
+        <td><br>0.951</td>
+        <td><br>0.933</td>
+        <td><br>0.997</td>
+        <td><br>0.992</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.986</td>
+        <td><br>0.966</td>
+        <td><br>0.938</td>
+        <td><br>0.938</td>
+        <td><br>0.998</td>
+        <td><br>0.992</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td>
+        <td><br>Precision</td>
+        <td><br>0.982</td>
+        <td><br>0.964</td>
+        <td><br>0.910</td>
+        <td><br>0.942</td>
+        <td><br>0.997</td>
+        <td><br>0.991</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.985</td>
+        <td><br>0.963</td>
+        <td><br>0.940</td>
+        <td><br>0.910</td>
+        <td><br>0.998</td>
+        <td><br>0.991</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.983</td>
+        <td><br>0.964</td>
+        <td><br>0.925</td>
+        <td><br>0.926</td>
+        <td><br>0.997</td>
+        <td><br>0.991</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
+        <td><br>Precision</td>
+        <td><br>0.971</td>
+        <td><br>0.947</td>
+        <td><br>0.866</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.969</td>
+        <td><br>0.943</td>
+        <td><br>0.891</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.970</td>
+        <td><br>0.945</td>
+        <td><br>0.878</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-4entities">NERmembert-base-4entities</a></td>
+        <td><br>Precision</td>
+        <td><br>0.970</td>
+        <td><br>0.944</td>
+        <td><br>0.872</td>
+        <td><br>0.878</td>
+        <td><br>0.996</td>
+        <td><br>0.986</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.969</td>
+        <td><br>0.947</td>
+        <td><br>0.880</td>
+        <td><br>0.866</td>
+        <td><br>0.996</td>
+        <td><br>0.986</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.970</td>
+        <td><br>0.945</td>
+        <td><br>0.876</td>
+        <td><br>0.872</td>
+        <td><br>0.996</td>
+        <td><br>0.986</td>
+    </tr>
+        <tr>
+        <td rowspan="3"><br>NERmembert-large-4entities (this model)</td>
+        <td><br>Precision</td>
+        <td><br>0.975</td>
+        <td><br>0.957</td>
+        <td><br>0.872</td>
+        <td><br>0.901</td>
+        <td><br>0.997</td>
+        <td><br>0.989</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.975</td>
+        <td><br>0.949</td>
+        <td><br>0.922</td>
+        <td><br>0.884</td>
+        <td><br>0.997</td>
+        <td><br>0.989</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.975</td>
+        <td><br>0.953</td>
+        <td><br>0.896</td>
+        <td><br>0.893</td>
+        <td><br>0.997</td>
+        <td><br>0.989</td>
+    </tr>
+</tbody>
+</table>
+</details>
+
+## Usage
+### Code
+
+```python
+from transformers import pipeline
+
+ner = pipeline('token-classification', model='CATIE-AQ/NERmembert-large-4entities', tokenizer='CATIE-AQ/NERmembert-large-4entities', aggregation_strategy="simple")
+
+results = ner(
+"Assurés de disputer l'Euro 2024 en Allemagne l'été prochain (du 14 juin au 14 juillet) depuis leur victoire aux Pays-Bas, les Bleus ont fait le nécessaire pour avoir des certitudes. Avec six victoires en six matchs officiels et un seul but encaissé, Didier Deschamps a consolidé les acquis de la dernière Coupe du monde. Les joueurs clés sont connus : Kylian Mbappé, Aurélien Tchouameni, Antoine Griezmann, Ibrahima Konaté ou encore Mike Maignan."
+)
+
+print(result)
+```
+```python
+
+```
+
+### Try it through Space
+A Space has been created to test the model. It is available [here](https://huggingface.co/spaces/CATIE-AQ/NERmembert).
 
-More information needed
 
 ## Training procedure
 
@@ -65,3 +993,93 @@ The following hyperparameters were used during training:
 - Pytorch 2.1.2
 - Datasets 2.16.1
 - Tokenizers 0.15.0
+
+
+## Environmental Impact
+
+*Carbon emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.*
+
+- **Hardware Type:** A100 PCIe 40/80GB
+- **Hours used:** 4h17min
+- **Cloud Provider:** Private Infrastructure
+- **Carbon Efficiency (kg/kWh):** 0.078 (estimated from [electricitymaps](https://app.electricitymaps.com/zone/FR) for the day of January 10, 2024.)
+- **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: 0.08 kg eq. CO2
+
+
+
+## Citations
+
+### NERmembert-large-4entities 
+```
+TODO
+```
+
+### multiconer
+
+> @inproceedings{multiconer2-report,  
+    title={{SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)}},  
+    author={Fetahu, Besnik and Kar, Sudipta and Chen, Zhiyu and Rokhlenko, Oleg and Malmasi, Shervin},  
+    booktitle={Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)},  
+    year={2023},  
+    publisher={Association for Computational Linguistics}}
+
+> @article{multiconer2-data,  
+    title={{MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition}},  
+    author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Rokhlenko, Oleg and Malmasi, Shervin},  
+    year={2023}}
+
+
+### multinerd
+
+> @inproceedings{tedeschi-navigli-2022-multinerd,  
+    title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)",  
+    author = "Tedeschi, Simone and  Navigli, Roberto",  
+    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",  
+    month = jul,  
+    year = "2022",  
+    address = "Seattle, United States",  
+    publisher = "Association for Computational Linguistics",  
+    url = "https://aclanthology.org/2022.findings-naacl.60",  
+    doi = "10.18653/v1/2022.findings-naacl.60",  
+    pages = "801--812"}
+
+### pii-masking-200k
+
+> @misc {ai4privacy_2023,  
+author = { {ai4Privacy} },  
+title = { pii-masking-200k (Revision 1d4c0a1) },  
+year = 2023,  
+url = { https://huggingface.co/datasets/ai4privacy/pii-masking-200k },  
+doi = { 10.57967/hf/1532 },  
+publisher = { Hugging Face }}
+
+### wikiner
+
+> @article{NOTHMAN2013151,  
+title = {Learning multilingual named entity recognition from Wikipedia},  
+journal = {Artificial Intelligence},  
+volume = {194},  
+pages = {151-175},  
+year = {2013},  
+note = {Artificial Intelligence, Wikipedia and Semi-Structured Resources},  
+issn = {0004-3702},  
+doi = {https://doi.org/10.1016/j.artint.2012.03.006},  
+url = {https://www.sciencedirect.com/science/article/pii/S0004370212000276},  
+author = {Joel Nothman and Nicky Ringland and Will Radford and Tara Murphy and James R. Curran}}
+
+
+### frenchNER_4entities
+```
+TODO
+```
+
+### CamemBERT
+> @inproceedings{martin2020camembert,  
+  title={CamemBERT: a Tasty French Language Model},  
+  author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},  
+  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},  
+  year={2020}}
+
+
+## License
+[cc-by-4.0](https://creativecommons.org/licenses/by/4.0/deed.en)