Update README.md

README.md

@@ -7,7 +7,7 @@ metrics:
 - f1
 - accuracy
 model-index:
-- name: Camembert-base-frenchNER_4entities
+- name: NERmembert-base-4entities
   results: []
 datasets:
 - CATIE-AQ/frenchNER_4entities
@@ -21,11 +21,11 @@ co2_eq_emissions: 20
 ---
 
 
-# Camembert-base-frenchNER_4entities
+# NERmembert-base-4entities
 
 ## Model Description
 
-We present **Camembert-base-frenchNER_4entities**, which is a [CamemBERT base](https://huggingface.co/camembert-base) fine-tuned for the Name Entity Recognition task for the French language on four French NER datasets for 4 entities (LOC, PER, ORG, MISC).  
+We present **NERmembert-base-4entities**, which is a [CamemBERT base](https://huggingface.co/camembert-base) 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/).
@@ -89,6 +89,60 @@ The evaluation was carried out using the [**evaluate**](https://pypi.org/project
 
 ### 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>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br>NERmembert-base-4entities (this model)</td>
+        <td><br><b>0.978</b></td>
+        <td><br><b>0.958</b></td>
+        <td><br><b>0.903</b></td>
+        <td><br><b>0.814</b></td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+    </tr>
+</tbody>
+</table>
+
+
+<details>
+<summary>Full results</summary>
 <table>
 <thead>
     <tr>
@@ -159,8 +213,36 @@ The evaluation was carried out using the [**evaluate**](https://pypi.org/project
         <td><br>0.989</td>
         <td><br>0.976</td>
     </tr>
-      <tr>
-        <td rowspan="3"><br>Camembert-base-frenchNER_4entities</td>
+    <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>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br>NERmembert-base-4entities (this model)</td>
         <td><br>Precision</td>
         <td><br>0.973</td>
         <td><br>0.951</td>
@@ -187,14 +269,95 @@ The evaluation was carried out using the [**evaluate**](https://pypi.org/project
         <td><br><b>0.993</b></td>
         <td><br><b>0.984</b></td>
     </tr>
+      <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td>
+        <td><br>Precision</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</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>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br>NERmembert-base-4entities (this model)</td>
+        <td><br><b>0.960</b></td>
+        <td><br><b>0.890</b></td>
+        <td><br><b>0.867</b></td>
+        <td><br><b>0.852</b></td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+    </tr>
+</tbody>
+</table>
+
+<details>
+<summary>Full results</summary>
 <table>
 <thead>
     <tr>
@@ -266,7 +429,35 @@ In detail:
         <td><br>0.881</td>
     </tr>
     <tr>
-        <td rowspan="3"><br>Camembert-base-frenchNER_4entities</td>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
+        <td><br>Precision</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br>NERmembert-base-4entities (this model)</td>
         <td><br>Precision</td>
         <td><br>0.954</td>
         <td><br>0.893</td>
@@ -293,11 +484,94 @@ In detail:
         <td><br><b>0.977</b></td>
         <td><br><b>0.954</b></td>
     </tr>
+        <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td>
+        <td><br>Precision</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</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>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br>NERmembert-base-4entities (this model)</td>
+        <td><br><b>0.985</b></td>
+        <td><br><b>0.973</b></td>
+        <td><br><b>0.938</b></td>
+        <td><br><b>0.770</b></td>
+    </tr>
+      <tr>
+        <td rowspan="1"><br><a href="https://hf/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+    </tr>
+</tbody>
+</table>
+
+<details>
+<summary>Full results</summary>
 <table>
 <thead>
     <tr>
@@ -369,8 +643,36 @@ In detail:
         <td><br>0.967</td>
     </tr>
     <tr>
-        <td rowspan="3"><br>Camembert-base-frenchNER_4entities</td>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
         <td><br>Precision</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+      <td rowspan="3"><br>NERmembert-base-4entities (this model)</td>
+      <td><br>Precision</td>
         <td><br>0.976</td>
         <td><br>0.961</td>
         <td><br>0.91</td>
@@ -396,12 +698,93 @@ In detail:
         <td><br><b>0.992</b></td>
         <td><br><b>0.983</b></td>
     </tr>
+        <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td>
+        <td><br>Precision</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</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><b>0.986</b></td>
+        <td><br><b>0.966</b></td>
+        <td><br><b>0.938</b></td>
+        <td><br><b>0.938</b></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>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+    </tr>
+    <tr>
+        <td rowspan="1"><br>NERmembert-base-4entities (this model)</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><a href="https://hf/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+    </tr>
+</tbody>
+</table>
+
+<details>
+<summary>Full results</summary>
 <table>
 <thead>
     <tr>
@@ -473,7 +856,35 @@ In detail:
         <td><br>0.991</td>
     </tr>
     <tr>
-        <td rowspan="3"><br>Camembert-base-frenchNER_4entities</td>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td>
+        <td><br>Precision</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>0</td>
+        <td><br>X</td>
+        <td><br>X</td>
+    </tr>
+    <tr>
+        <td rowspan="3"><br>NERmembert-base-4entities (this model)</td>
         <td><br>Precision</td>
         <td><br>0.970</td>
         <td><br>0.944</td>
@@ -500,9 +911,37 @@ In detail:
         <td><br>0.996</td>
         <td><br>0.986</td>
     </tr>
+        <tr>
+        <td rowspan="3"><br><a href="https://hf/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td>
+        <td><br>Precision</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</td>
+    </tr>
+    <tr>
+        <td><br>Recall</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</td>
+    </tr>
+    <tr>
+        <td>F1</td>
+        <td><br>A</td>
+        <td><br>B</td>
+        <td><br>C</td>
+        <td><br>D</td>
+        <td><br>E</td>
+        <td><br>F</td>
+    </tr>
 </tbody>
 </table>
-
+</details>
 
 ## Usage
 ### Code
@@ -510,7 +949,7 @@ In detail:
 ```python
 from transformers import pipeline
 
-ner = pipeline('question-answering', model='CATIE-AQ/Camembert-base-frenchNER_4entities', tokenizer='CATIE-AQ/Camembert-base-frenchNER_4entities', aggregation_strategy="simple")
+ner = pipeline('question-answering', model='CATIE-AQ/NERmembert-base-4entities', tokenizer='CATIE-AQ/NERmembert-base-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."
@@ -576,7 +1015,7 @@ print(result)
 ```
 
 ### Try it through Space
-A Space has been created to test the model. It is available [here](https://huggingface.co/spaces/CATIE-AQ/Camembert-NER).
+A Space has been created to test the model. It is available [here](https://huggingface.co/spaces/CATIE-AQ/NERmembert).
 
 
 ## Training procedure