Update README.md

README.md

@@ -1,92 +1,1334 @@
 ---
-library_name: transformers
 license: mit
 base_model: almanach/camembertav2-base
-tags:
-- generated_from_trainer
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: camembertav2-base-frenchNER_3entities
+- name: NERmemberta-3entities
   results: []
+datasets:
+- CATIE-AQ/frenchNER_3entities
+language:
+- fr
+widget:
+- text: "Le dévoilement du logo officiel des JO s'est déroulé le 21 octobre 2019 au Grand Rex. Ce nouvel emblème et cette nouvelle typographie ont été conçus par le designer Sylvain Boyer avec les agences Royalties & Ecobranding. Rond, il rassemble trois symboles : une médaille d'or, la flamme olympique et Marianne, symbolisée par un visage de femme mais privée de son bonnet phrygien caractéristique. La typographie dessinée fait référence à l'Art déco, mouvement artistique des années 1920, décennie pendant laquelle ont eu lieu pour la dernière fois les Jeux olympiques à Paris en 1924. Pour la première fois, ce logo sera unique pour les Jeux olympiques et les Jeux paralympiques."
+library_name: transformers
+pipeline_tag: token-classification
+co2_eq_emissions: 14
 ---
 
+
+# NERmemBERT2-3entities
+
+## Model Description
+
+We present **NERmemBERTa-3entities**, which is a [CamemBERTa v2 base](https://huggingface.co/almanach/camembertav2-base) fine-tuned for the Name Entity Recognition task for the French language on five French NER datasets for 3 entities (LOC, PER, ORG).  
+All these datasets were concatenated and cleaned into a single dataset that we called [frenchNER_3entities](https://huggingface.co/datasets/CATIE-AQ/frenchNER_3entities).  
+This represents a total of over **420,264 rows, of which 346,071 are for training, 32,951 for validation and 41,242 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/).  
+
+
+## Evaluation results
+
+### frenchNER_3entities
+
+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>
+    </tr>
+</thead>
+<tbody>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner (110M)</a></td>
+        <td><br>0.941</td>
+        <td><br>0.883</td>
+        <td><br>0.658</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner (67.5M)</a></td>
+        <td><br>0.942</td>
+        <td><br>0.882</td>
+        <td><br>0.647</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities (110M)</a></td>
+        <td><br>0.966</td>
+        <td><br>0.940</td>
+        <td><br>0.876</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert2-3entities">NERmembert2-3entities (111M)</a></td>
+        <td><br>0.967</td>
+        <td><br>0.942</td>
+        <td><br>0.875</td>
+    </tr>
+        <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmemberta-3entities (111M) (this model)</a></td>
+        <td><br><b>0.970</b></td>
+        <td><br>0.943</td>
+        <td><br>0.881</td>
+      </tr>
+      <tr>
+      <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities (336M)</a></td>
+        <td><br>0.969</td>
+        <td><br><b>0.947</b></td>
+        <td><br><b>0.890</b></td>
+    </tr>
+      </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>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 (110M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.918</td>
+        <td><br>0.860</td>
+        <td><br>0.831</td>
+        <td><br>0.992</td>
+        <td><br>0.974</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.964</td>
+        <td><br>0.908</td>
+        <td><br>0.544</td>
+        <td><br>0.964</td>
+        <td><br>0.948</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.941</td>
+        <td><br>0.883</td>
+        <td><br>0.658</td>
+        <td><br>0.978</td>
+        <td><br>0.961</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner (67.5M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.929</td>
+        <td><br>0.861</td>
+        <td><br>0.813</td>
+        <td><br>0.991</td>
+        <td><br>0.974</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.956</td>
+        <td><br>0.905</td>
+        <td><br>0.956</td>
+        <td><br>0.965</td>
+        <td><br>0.948</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.942</td>
+        <td><br>0.882</td>
+        <td><br>0.647</td>
+        <td><br>0.978</td>
+        <td><br>0.961</td>
+    </tr>
+    <tr>
+      <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities (110M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.961</td>
+        <td><br>0.935</td>
+        <td><br>0.877</td>
+        <td><br>0.995</td>
+        <td><br>0.986</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.972</td>
+        <td><br>0.946</td>
+        <td><br>0.876</td>
+        <td><br>0.994</td>
+        <td><br>0.986</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.966</td>
+        <td><br>0.940</td>
+        <td><br>0.876</td>
+        <td><br>0.994</td>
+        <td><br>0.986</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert2-3entities">NERmembert2-3entities (111M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.964</td>
+        <td><br>0.935</td>
+        <td><br>0.872</td>
+        <td><br>0.995</td>
+        <td><br>0.985</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.967</td>
+        <td><br>0.949</td>
+        <td><br>0.878</td>
+        <td><br>0.993</td>
+        <td><br>0.984</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.967</td>
+        <td><br>0.942</td>
+        <td><br>0.875</td>
+        <td><br>0.994</td>
+        <td><br>0.985</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmemberta-3entities (111M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.966</td>
+        <td><br>0.934</td>
+        <td><br>0.880</td>
+        <td><br>0.995</td>
+        <td><br>0.985</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.973</td>
+        <td><br>0.952</td>
+        <td><br>0.883</td>
+        <td><br>0.993</td>
+        <td><br>0.985</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.970</td>
+        <td><br>0.943</td>
+        <td><br>0.881</td>
+        <td><br>0.994</td>
+        <td><br>0.985</td>
+    </tr>
+        <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities (336M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.946</td>
+        <td><br>0.884</td>
+        <td><br>0.859</td>
+        <td><br>0.993</td>
+        <td><br>0.971</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.955</td>
+        <td><br>0.904</td>
+        <td><br>0.550</td>
+        <td><br>0.993</td>
+        <td><br>0.971</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.951</td>
+        <td><br>0.894</td>
+        <td><br>0.671</td>
+        <td><br>0.988</td>
+        <td><br>0.971</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>
+    </tr>
+</thead>
+<tbody>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner (110M)</a></td>
+        <td><br>0.940</td>
+        <td><br>0.761</td>
+        <td><br>0.723</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner (67.5M)</a></td>
+        <td><br>0.921</td>
+        <td><br>0.748</td>
+        <td><br>0.694</td>
+    </tr>
+    <tr>
+      <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities (110M)</a></td>
+        <td><br>0.960</td>
+        <td><br>0.887</td>
+        <td><br>0.876</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmembert2-3entities (111M)</a></td>
+        <td><br>0.958</td>
+        <td><br>0.876</td>
+        <td><br>0.863</td>
+    </tr>
+        <tr>
+        <td rowspan="1"><br>NERmemberta-3entities (111M) (this model)</td>
+        <td><br>0.964</td>
+        <td><br>0.865</td>
+        <td><br>0.859</td>
+      </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities (336M)</a></td>
+        <td><br><b>0.965</b></td>
+        <td><br><b>0.902</b></td>
+        <td><br><b>0.896</b></td>
+    </tr>
+    </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>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 (110M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.908</td>
+        <td><br>0.717</td>
+        <td><br>0.753</td>
+        <td><br>0.987</td>
+        <td><br>0.947</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.975</td>
+        <td><br>0.811</td>
+        <td><br>0.696</td>
+        <td><br>0.878</td>
+        <td><br>0.880</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.940</td>
+        <td><br>0.761</td>
+        <td><br>0.723</td>
+        <td><br>0.929</td>
+        <td><br>0.912</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner (67.5M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.885</td>
+        <td><br>0.738</td>
+        <td><br>0.737</td>
+        <td><br>0.983</td>
+        <td><br>0.943</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.960</td>
+        <td><br>0.759</td>
+        <td><br>0.655</td>
+        <td><br>0.882</td>
+        <td><br>0.877</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.921</td>
+        <td><br>0.748</td>
+        <td><br>0.694</td>
+        <td><br>0.930</td>
+        <td><br>0.909</td>
+    </tr>
+    <tr>
+      <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities (110M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.957</td>
+        <td><br>0.894</td>
+        <td><br>0.876</td>
+        <td><br>0.986</td>
+        <td><br>0.972</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.962</td>
+        <td><br>0.880</td>
+        <td><br>0.878</td>
+        <td><br>0.985</td>
+        <td><br>0.972</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.960</td>
+        <td><br>0.887</td>
+        <td><br>0.876</td>
+        <td><br>0.985</td>
+        <td><br>0.972</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert2-3entities">NERmembert2-3entities (111M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.951</td>
+        <td><br>0.906</td>
+        <td><br>0.853</td>
+        <td><br>0.984</td>
+        <td><br>0.967</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.966</td>
+        <td><br>0.848</td>
+        <td><br>0.874</td>
+        <td><br>0.984</td>
+        <td><br>0.967</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.958</td>
+        <td><br>0.876</td>
+        <td><br>0.863</td>
+        <td><br>0.984</td>
+        <td><br>0.967</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmemberta-3entities (111M) (this model)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.962</td>
+        <td><br>0.859</td>
+        <td><br>0.862</td>
+        <td><br>0.985</td>
+        <td><br>0.970</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.967</td>
+        <td><br>0.871</td>
+        <td><br>0.857</td>
+        <td><br>0.984</td>
+        <td><br>0.970</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.964</td>
+        <td><br>0.865</td>
+        <td><br>0.859</td>
+        <td><br>0.985</td>
+        <td><br>0.970</td>
+    </tr>
+          <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities (336M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.960</td>
+        <td><br>0.903</td>
+        <td><br>0.916</td>
+        <td><br>0.987</td>
+        <td><br>0.976</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.969</td>
+        <td><br>0.900</td>
+        <td><br>0.877</td>
+        <td><br>0.987</td>
+        <td><br>0.976</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.965</td>
+        <td><br>0.902</td>
+        <td><br>0.896</td>
+        <td><br>0.987</td>
+        <td><br>0.976</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>
+    </tr>
+</thead>
+<tbody>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner (110M)</a></td>
+        <td><br>0.962</td>
+        <td><br>0.934</td>
+        <td><br>0.888</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner (67.5M)</a></td>
+        <td><br>0.972</td>
+        <td><br>0.938</td>
+        <td><br>0.884</td>
+    </tr>
+    <tr>
+      <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities (110M)</a></td>
+        <td><br>0.985</td>
+        <td><br>0.973</td>
+        <td><br>0.938</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmembert2-3entities (111M)</a></td>
+        <td><br>0.985</td>
+        <td><br>0.972</td>
+        <td><br>0.933</td>
+    </tr>
+        <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmemberta-3entities (111M) (this model)</a></td>
+        <td><br>0.986</td>
+        <td><br>0.974</td>
+        <td><br>0.945</td>
+      </tr>
+      <tr>
+      <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities (336M)</a></td>
+        <td><br><b>0.987</b></td>
+        <td><br><b>0.979</b></td>
+        <td><br><b>0.953</b></td>
+    </tr>
+    </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>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 (110M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.931</td>
+        <td><br>0.893</td>
+        <td><br>0.827</td>
+        <td><br>0.999</td>
+        <td><br>0.988</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.994</td>
+        <td><br>0.980</td>
+        <td><br>0.959</td>
+        <td><br>0.973</td>
+        <td><br>0.974</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.962</td>
+        <td><br>0.934</td>
+        <td><br>0.888</td>
+        <td><br>0.986</td>
+        <td><br>0.981</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner (67.5M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.954</td>
+        <td><br>0.908</td>
+        <td><br>0.817</td>
+        <td><br>0.999</td>
+        <td><br>0.990</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.991</td>
+        <td><br>0.969</td>
+        <td><br>0.963</td>
+        <td><br>0.975</td>
+        <td><br>0.975</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.972</td>
+        <td><br>0.938</td>
+        <td><br>0.884</td>
+        <td><br>0.987</td>
+        <td><br>0.983</td>
+    </tr>
+    <tr>
+      <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities (110M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.974</td>
+        <td><br>0.965</td>
+        <td><br>0.910</td>
+        <td><br>0.999</td>
+        <td><br>0.995</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.995</td>
+        <td><br>0.981</td>
+        <td><br>0.968</td>
+        <td><br>0.996</td>
+        <td><br>0.995</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.985</td>
+        <td><br>0.973</td>
+        <td><br>0.938</td>
+        <td><br>0.998</td>
+        <td><br>0.995</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert2-3entities">NERmembert2-3entities (111M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.975</td>
+        <td><br>0.960</td>
+        <td><br>0.902</td>
+        <td><br>0.999</td>
+        <td><br>0.995</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.995</td>
+        <td><br>0.985</td>
+        <td><br>0.967</td>
+        <td><br>0.995</td>
+        <td><br>0.995</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.985</td>
+        <td><br>0.972</td>
+        <td><br>0.933</td>
+        <td><br>0.997</td>
+        <td><br>0.995</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmemberta-3entities (111M) (this model)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.976</td>
+        <td><br>0.961</td>
+        <td><br>0.915</td>
+        <td><br>0.999</td>
+        <td><br>0.995</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.997</td>
+        <td><br>0.987</td>
+        <td><br>0.976</td>
+        <td><br>0.996</td>
+        <td><br>0.995</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.986</td>
+        <td><br>0.974</td>
+        <td><br>0.945</td>
+        <td><br>0.997</td>
+        <td><br>0.995</td>
+    </tr>
+          <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities (336M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.979</td>
+        <td><br>0.970</td>
+        <td><br>0.927</td>
+        <td><br>0.999</td>
+        <td><br>0.996</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.996</td>
+        <td><br>0.987</td>
+        <td><br>0.980</td>
+        <td><br>0.997</td>
+        <td><br>0.996</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br><b>0.987</b></td>
+        <td><br><b>0.979</b></td>
+        <td><br><b>0.953</b></td>
+        <td><br><b>0.998</b></td>
+        <td><br><b>0.996</b></td>
+    </tr>
+    </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>
+    </tr>
+</thead>
+<tbody>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner (110M)</a></td>
+        <td><br><b>0.986</b></td>
+        <td><br><b>0.966</b></td>
+        <td><br><b>0.938</b></td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner (67.5M)</a></td>
+        <td><br>0.983</td>
+        <td><br>0.964</td>
+        <td><br>0.925</td>
+    </tr>
+    <tr>
+      <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities (110M)</a></td>
+        <td><br>0.969</td>
+        <td><br>0.945</td>
+        <td><br>0.878</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmembert2-3entities (111M)</a></td>
+        <td><br>0.969</td>
+        <td><br>0.946</td>
+        <td><br>0.866</td>
+    </tr>
+        <tr>
+        <td rowspan="1"><br>NERmemberta-3entities (111M) (this model)</td>
+        <td><br>0.971</td>
+        <td><br>0.948</td>
+        <td><br>0.885</td>
+      </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities (336M)</a></td>
+        <td><br>0.972</td>
+        <td><br>0.950</td>
+        <td><br>0.893</td>
+    </tr>
+    </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>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 (110M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.986</td>
+        <td><br>0.962</td>
+        <td><br>0.925</td>
+        <td><br>0.999</td>
+        <td><br>0.994</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.987</td>
+        <td><br>0.969</td>
+        <td><br>0.951</td>
+        <td><br>0.965</td>
+        <td><br>0.967</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br><b>0.986</b></td>
+        <td><br><b>0.966</b></td>
+        <td><br><b>0.938</b></td>
+        <td><br><b>0.982</b></td>
+        <td><br><b>0.980</b></td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner (67.5M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.982</td>
+        <td><br>0.951</td>
+        <td><br>0.910</td>
+        <td><br>0.998</td>
+        <td><br>0.994</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.985</td>
+        <td><br>0.963</td>
+        <td><br>0.940</td>
+        <td><br>0.966</td>
+        <td><br>0.967</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.983</td>
+        <td><br>0.964</td>
+        <td><br>0.925</td>
+        <td><br>0.982</td>
+        <td><br>0.80</td>
+    </tr>
+    <tr>
+      <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities (110M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.971</td>
+        <td><br>0.947</td>
+        <td><br>0.866</td>
+        <td><br>0.994</td>
+        <td><br>0.989</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.969</td>
+        <td><br>0.942</td>
+        <td><br>0.891</td>
+        <td><br>0.995</td>
+        <td><br>0.989</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.969</td>
+        <td><br>0.945</td>
+        <td><br>0.878</td>
+        <td><br>0.995</td>
+        <td><br>0.989</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert2-3entities">NERmembert2-3entities (111M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.971</td>
+        <td><br>0.946</td>
+        <td><br>0.863 </td>
+        <td><br>0.994</td>
+        <td><br>0.988</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.967</td>
+        <td><br>0.946</td>
+        <td><br>0.870</td>
+        <td><br>0.995</td>
+        <td><br>0.988</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.969</td>
+        <td><br>0.946</td>
+        <td><br>0.866</td>
+        <td><br>0.994</td>
+        <td><br>0.988</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmemberta-3entities (111M) (this model)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.972</td>
+        <td><br>0.946</td>
+        <td><br>0.865</td>
+        <td><br>0.995</td>
+        <td><br>0.987</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.970</td>
+        <td><br>0.950</td>
+        <td><br>0.905</td>
+        <td><br>0.995</td>
+        <td><br>0.987</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.971</td>
+        <td><br>0.948</td>
+        <td><br>0.885</td>
+        <td><br>0.995</td>
+        <td><br>0.987</td>
+    </tr>
+          <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities (336M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.973</td>
+        <td><br>0.953</td>
+        <td><br>0.873</td>
+        <td><br>0.996</td>
+        <td><br>0.990</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.990</td>
+        <td><br>0.948</td>
+        <td><br>0.913</td>
+        <td><br>0.995</td>
+        <td><br>0.990</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.972</td>
+        <td><br>0.950</td>
+        <td><br>0.893</td>
+        <td><br>0.996</td>
+        <td><br>0.990</td>
+    </tr>
+    </tr>
+</tbody>
+</table>
+</details>
+
+### wikiann
+
+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>
+    </tr>
+</thead>
+<tbody>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner (110M)</a></td>
+        <td><br>0.867</td>
+        <td><br>0.722</td>
+        <td><br>0.451</td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner (67.5M)</a></td>
+        <td><br>0.862</td>
+        <td><br>0.722</td>
+        <td><br>0.451</td>
+    </tr>
+    <tr>
+      <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities (110M)</a></td>
+        <td><br>0.947</td>
+        <td><br>0.906</td>
+        <td><br>0.886</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmembert2-3entities (111M)</a></td>
+        <td><br>0.950</td>
+        <td><br>0.911</td>
+        <td><br><b>0.910</b></td>
+    </tr>
+        <tr>
+        <td rowspan="1"><br>NERmemberta-3entities (111M) (this model)</td>
+        <td><br><b>0.953</b></td>
+        <td><br>0.902</td>
+        <td><br>0.890</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities (336M)</a></td>
+        <td><br>0.949</td>
+        <td><br><b>0.912</b></td>
+        <td><br>0.899</td>
+    </tr>
+    </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>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 (110M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.862</td>
+        <td><br>0.700</td>
+        <td><br>0.864</td>
+        <td><br>0.867</td>
+        <td><br>0.832</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.871</td>
+        <td><br>0.746</td>
+        <td><br>0.305</td>
+        <td><br>0.950</td>
+        <td><br>0.772</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.867</td>
+        <td><br>0.722</td>
+        <td><br>0.451</td>
+        <td><br>0.867</td>
+        <td><br>0.801</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner (67.5M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.862</td>
+        <td><br>0.700</td>
+        <td><br>0.864</td>
+        <td><br>0.867</td>
+        <td><br>0.832</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.871</td>
+        <td><br>0.746</td>
+        <td><br>0.305</td>
+        <td><br>0.950</td>
+        <td><br>0.772</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.867</td>
+        <td><br>0.722</td>
+        <td><br>0.451</td>
+        <td><br>0.907</td>
+        <td><br>0.800</td>
+    </tr>
+    <tr>
+      <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities (110M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.948</td>
+        <td><br>0.900</td>
+        <td><br>0.893</td>
+        <td><br>0.979</td>
+        <td><br>0.942</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.946</td>
+        <td><br>0.911</td>
+        <td><br>0.878</td>
+        <td><br>0.982</td>
+        <td><br>0.942</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.947</td>
+        <td><br>0.906</td>
+        <td><br>0.886</td>
+        <td><br>0.980</td>
+        <td><br>0.942</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert2-3entities">NERmembert2-3entities (111M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.962</td>
+        <td><br>0.906</td>
+        <td><br>0.890</td>
+        <td><br>0.971</td>
+        <td><br>0.941</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.938</td>
+        <td><br>0.917</td>
+        <td><br>0.884</td>
+        <td><br>0.982</td>
+        <td><br>0.941</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.950</td>
+        <td><br>0.911</td>
+        <td><br>0.887</td>
+        <td><br>0.976</td>
+        <td><br>0.941</td>
+    </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-3entities">NERmemberta-3entities (111M) (this model)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.961</td>
+        <td><br>0.902</td>
+        <td><br>0.899</td>
+        <td><br>0.972</td>
+        <td><br>0.942</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.946</td>
+        <td><br>0.918</td>
+        <td><br>0.881</td>
+        <td><br>0.982</td>
+        <td><br>0.942</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>0.953</td>
+        <td><br>0.902</td>
+        <td><br>0.890</td>
+        <td><br>0.977</td>
+        <td><br>0.942</td>
+    </tr>
+        <tr>
+       <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities (336M)</a></td>
+        <td><br>Precision</td>
+        <td><br>0.958</td>
+        <td><br>0.917</td>
+        <td><br>0.897</td>
+        <td><br>0.980</td>
+        <td><br><b>0.948</b></td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>0.940</td>
+        <td><br>0.915</td>
+        <td><br>0.901</td>
+        <td><br>0.983</td>
+        <td><br><b>0.948</b></td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br><b>0.949</b></td>
+        <td><br><b>0.912</b></td>
+        <td><br><b>0.899</b></td>
+        <td><br><b>0.983</b></td>
+        <td><br><b>0.948</b></td>
+    </tr>
+</tbody>
+</table>
+</details>
+
+
+## Usage
+### Code
+
+```python
+from transformers import pipeline
+
+ner = pipeline('token-classification', model='CATIE-AQ/NERmembert2-base-3entities', tokenizer='CATIE-AQ/NERmembert2-base-3entities', aggregation_strategy="simple")
+
+result = ner(
+"Le dévoilement du logo officiel des JO s'est déroulé le 21 octobre 2019 au Grand Rex. Ce nouvel emblème et cette nouvelle typographie ont été conçus par le designer Sylvain Boyer avec les agences Royalties & Ecobranding. Rond, il rassemble trois symboles : une médaille d'or, la flamme olympique et Marianne, symbolisée par un visage de femme mais privée de son bonnet phrygien caractéristique. La typographie dessinée fait référence à l'Art déco, mouvement artistique des années 1920, décennie pendant laquelle ont eu lieu pour la dernière fois les Jeux olympiques à Paris en 1924. Pour la première fois, ce logo sera unique pour les Jeux olympiques et les Jeux paralympiques."
+)
+
+print(result)
 ```
-{'LOC': {'precision': 0.9341895320551385,
-  'recall': 0.9516260108445131,
-  'f1': 0.9428271615530316,
-  'number': 75061},
- 'O': {'precision': 0.9953844747581743,
-  'recall': 0.9930766705362066,
-  'f1': 0.9942292334305959,
-  'number': 932066},
- 'ORG': {'precision': 0.8804077936494026,
-  'recall': 0.8825734282116606,
-  'f1': 0.8814892808048901,
-  'number': 34149},
- 'PER': {'precision': 0.9657491578607356,
-  'recall': 0.973339689331225,
-  'f1': 0.9695295670905427,
-  'number': 86008},
- 'overall_precision': 0.985463290528385,
- 'overall_recall': 0.985463290528385,
- 'overall_f1': 0.985463290528385,
- 'overall_accuracy': 0.985463290528385}
-```
- 
-<!-- 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. -->
 
-# camembertav2-base-frenchNER_3entities
 
-This model is a fine-tuned version of [almanach/camembertav2-base](https://huggingface.co/almanach/camembertav2-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0880
-- Precision: 0.9859
-- Recall: 0.9859
-- F1: 0.9859
-- Accuracy: 0.9859
+### Try it through Space
+A Space has been created to test the model. It is available [here](https://huggingface.co/spaces/CATIE-AQ/NERmembert).
 
-## Model description
 
-More information needed
+## Environmental Impact
 
-## Intended uses & limitations
+*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.*
 
-More information needed
+- **Hardware Type:** A100 PCIe 40/80GB
+- **Hours used:** 1h51min
+- **Cloud Provider:** Private Infrastructure
+- **Carbon Efficiency (kg/kWh):** 0.055 (estimated from [electricitymaps](https://app.electricitymaps.com/zone/FR) for the day of November 21, 2024.)
+- **Carbon Emitted** *(Power consumption x Time x Carbon produced based on location of power grid)*: 0.014 kg eq. CO2
 
-## Training and evaluation data
 
-More information needed
 
-## Training procedure
+## Citations
 
-### Training hyperparameters
+### NERmemBERT2-base-3entities
+```
+@misc {NERmemberta2024,
+    author       = { {BOURDOIS, Loïck} },  
+    organization = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
+	title        = { NERmemberta-base-3entities },
+	year         = 2024,
+	url          = { https://huggingface.co/CATIE-AQ/NERmemberta-base-3entities },
+	doi          = { 10.57967/hf/1752 },
+	publisher    = { Hugging Face }
+}
+```
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 3
+### NERmemBERT
+```
+@misc {NERmembert2024,
+    author       = { {BOURDOIS, Loïck} },  
+    organization = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
+	title        = { NERmembert-base-3entities },
+	year         = 2024,
+	url          = { https://huggingface.co/CATIE-AQ/NERmembert-base-3entities },
+	doi          = { 10.57967/hf/1752 },
+	publisher    = { Hugging Face }
+}
+```
 
-### Training results
+### 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}}
+```
 
-| Training Loss | Epoch | Step   | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0335        | 1.0   | 43650  | 0.0854          | 0.9833    | 0.9833 | 0.9833 | 0.9833   |
-| 0.0169        | 2.0   | 87300  | 0.0821          | 0.9854    | 0.9854 | 0.9854 | 0.9854   |
-| 0.0103        | 3.0   | 130950 | 0.0880          | 0.9859    | 0.9859 | 0.9859 | 0.9859   |
+### CamemBERT 2.0
+```
+@misc{antoun2024camembert20smarterfrench,
+      title={CamemBERT 2.0: A Smarter French Language Model Aged to Perfection}, 
+      author={Wissam Antoun and Francis Kulumba and Rian Touchent and Éric de la Clergerie and Benoît Sagot and Djamé Seddah},
+      year={2024},
+      eprint={2411.08868},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2411.08868}, 
+}
+```
 
+### 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 }}
+```
 
-### Framework versions
+### wikiann
+```
+@inproceedings{rahimi-etal-2019-massively,  
+    title = "Massively Multilingual Transfer for {NER}",  
+    author = "Rahimi, Afshin and Li, Yuan and Cohn, Trevor",  
+    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",  
+    month = jul,  
+    year = "2019",  
+    address = "Florence, Italy",  
+    publisher = "Association for Computational Linguistics",  
+    url = "https://www.aclweb.org/anthology/P19-1015",  
+    pages = "151--164"}
+```
+
+### 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_3entities
+```
+@misc {frenchNER2024,  
+    author       = { {BOURDOIS, Loïck} },  
+    organization  = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
+    title        = { frenchNER_3entities },  
+    year         = 2024,  
+    url          = { https://huggingface.co/CATIE-AQ/frenchNER_3entities },  
+    doi          = { 10.57967/hf/1751 },  
+    publisher    = { Hugging Face }  
+}
+```
 
-- Transformers 4.46.1
-- Pytorch 2.4.0+cu121
-- Datasets 2.21.0
-- Tokenizers 0.20.1
+## License
+MIT