

# NERmembert-large-4entities

## 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/).



## Dataset

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 de football" |

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.co/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.co/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.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td>
        <td><br>0.980</td>
        <td><br>0.963</td>
        <td><br>0.919</td>
        <td><br>0</td>
    </tr>
    <tr>
        <td rowspan="1"><br><a href="https://hf.co/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><b>0.982</b></td>
        <td><br><b>0.964</b></td>
        <td><br><b>0.919</b></td>
        <td><br><b>0.834</b></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.co/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.co/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.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td>
        <td><br>Precision</td>
        <td><br>0.978</td>
        <td><br>0.960</td>
        <td><br>0.899</td>
        <td><br>0</td>
        <td><br>X</td>
        <td><br>X</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.985</td>
        <td><br>0.966</td>
        <td><br>0.940</td>
        <td><br>0</td>
        <td><br>X</td>
        <td><br>X</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.980</td>
        <td><br>0.963</td>
        <td><br>0.919</td>
        <td><br>0</td>
        <td><br>X</td>
        <td><br>X</td>
    </tr>
    <tr>
        <td rowspan="3"><br><a href="https://hf.co/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><b>0.982</b></td>
        <td><br><b>0.964</b></td>
        <td><br><b>0.919</b></td>
        <td><br><b>0.834</b></td>
        <td><br><b>0.994</b></td>
        <td><br><b>0.986</b></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.co/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.co/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.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td>
        <td><br>0.965</td>
        <td><br>0.902</td>
        <td><br>0.896</td>
        <td><br>0</td>
    </tr>
    <tr>
        <td rowspan="1"><br><a href="https://hf.co/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><b>0.969</b></td>
        <td><br><b>0.919</b></td>
        <td><br><b>0.904</b></td>
        <td><br><b>0.864</b></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.co/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.co/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.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td>
        <td><br>Precision</td>
        <td><br>0.960</td>
        <td><br>0.903</td>
        <td><br>0.916</td>
        <td><br>0</td>
        <td><br>X</td>
        <td><br>X</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.970</td>
        <td><br>0.900</td>
        <td><br>0.877</td>
        <td><br>0</td>
        <td><br>X</td>
        <td><br>X</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.965</td>
        <td><br>0.902</td>
        <td><br>0.896</td>
        <td><br>0</td>
        <td><br>X</td>
        <td><br>X</td>
    </tr>
    <tr>
        <td rowspan="3"><br><a href="https://hf.co/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><b>0.969</b></td>
        <td><br><b>0.919</b></td>
        <td><br><b>0.904</b></td>
        <td><br><b>0.864</b></td>
        <td><br><b>0.978</b></td>
        <td><br><b>0.961</b></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.co/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.co/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.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td>
        <td><br><b>0.987</b></td>
        <td><br><b>0.979</b></td>
        <td><br><b>0.953</b></td>
        <td><br>0</td>
    </tr>
    <tr>
        <td rowspan="1"><br><a href="https://hf.co/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><b>0.987</b></td>
        <td><br>0.976</td>
        <td><br>0.948</td>
        <td><br><b>0.790</b></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.co/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.co/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.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td>
        <td><br>Precision</td>
        <td><br>0.979</td>
        <td><br>0.970</td>
        <td><br>0.927</td>
        <td><br>0</td>
        <td><br>X</td>
        <td><br>X</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</td>
        <td><br>X</td>
        <td><br>X</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>0</td>
        <td><br>X</td>
        <td><br>X</td>
    </tr>
    <tr>
      <td rowspan="3"><br><a href="https://hf.co/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>0.911</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><b>0.987</b></td>
        <td><br>0.976</td>
        <td><br>0.948</td>
        <td><br><b>0.790</b></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><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.co/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.co/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.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td>
        <td><br>0.972</td>
        <td><br>0.953</td>
        <td><br>0.893</td>
        <td><br>0</td>
    </tr>
    <tr>
        <td rowspan="1"><br><a href="https://hf.co/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><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>
        <td><br><b>0.998</b></td>
        <td><br><b>0.992</b></td>
    </tr>
      <tr>
        <td rowspan="3"><br><a href="https://hf.co/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.co/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.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td>
        <td><br>Precision</td>
        <td><br>0.973</td>
        <td><br>0.953</td>
        <td><br>0.873</td>
        <td><br>0</td>
        <td><br>X</td>
        <td><br>X</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.971</td>
        <td><br>0.948</td>
        <td><br>0.913</td>
        <td><br>0</td>
        <td><br>X</td>
        <td><br>X</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.972</td>
        <td><br>0.953</td>
        <td><br>0.893</td>
        <td><br>0</td>
        <td><br>X</td>
        <td><br>X</td>
    </tr>
    <tr>
        <td rowspan="3"><br><a href="https://hf.co/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(
"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)
```
```python
[{'entity_group': 'MISC', 'score': 0.9922348, 'word': 'JO', 'start': 35, 'end': 38},
{'entity_group': 'LOC', 'score': 0.9995632, 'word': 'Grand Rex', 'start': 74, 'end': 84},
{'entity_group': 'PER', 'score': 0.99612623, 'word': 'Sylvain Boyer', 'start': 164, 'end': 178},
{'entity_group': 'ORG', 'score': 0.87376696, 'word': 'Royalties & Ecobranding', 'start': 195, 'end': 219},
{'entity_group': 'PER', 'score': 0.8121169, 'word': 'Marianne', 'start': 298, 'end': 307},
{'entity_group': 'MISC', 'score': 0.7016645, 'word': 'Art déco', 'start': 438, 'end': 446},
{'entity_group': 'MISC', 'score': 0.99880475, 'word': 'Jeux olympiques', 'start': 549, 'end': 565},
{'entity_group': 'LOC', 'score': 0.98473144, 'word': 'Paris', 'start': 567, 'end': 573},
{'entity_group': 'MISC', 'score': 0.99421215, 'word': 'Jeux olympiques', 'start': 634, 'end': 650},
{'entity_group': 'MISC', 'score': 0.9936283, 'word': 'Jeux paralympiques', 'start': 657, 'end': 676}]
```

### Try it through Space
A Space has been created to test the model. It is available [here](https://huggingface.co/spaces/CATIE-AQ/NERmembert).


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0347        | 1.0   | 41095  | 0.0537          | 0.9832    | 0.9832 | 0.9832 | 0.9832   |
| 0.0237        | 2.0   | 82190  | 0.0448          | 0.9858    | 0.9858 | 0.9858 | 0.9858   |
| 0.0119        | 3.0   | 123285 | 0.0532          | 0.9860    | 0.9860 | 0.9860 | 0.9860   |


### Framework versions

- Transformers 4.36.2
- 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 
```
@misc {NERmembert2024,
    author       = { {BOURDOIS, Loïck} },  
    organization = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { NERmembert-large-4entities (Revision 1cd8be5) },
	year         = 2024,
	url          = { https://huggingface.co/CATIE-AQ/NERmembert-large-4entities },
	doi          = { 10.57967/hf/1752 },
	publisher    = { Hugging Face }
}
```

### 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 }}
```

### 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_4entities
```
@misc {frenchNER2024,  
    author       = { {BOURDOIS, Loïck} },  
    organization  = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
    title        = { frenchNER_4entities },  
    year         = 2024,  
    url          = { https://huggingface.co/CATIE-AQ/frenchNER_4entities },  
    doi          = { 10.57967/hf/1751 },  
    publisher    = { Hugging Face }  
}
```


### 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)
{"language": ["fr"], "license": "cc-by-4.0", "library_name": "transformers", "datasets": ["CATIE-AQ/frenchNER_4entities"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "camembert/camembert-large", "widget": [{"text": "Le d\u00e9voilement du logo officiel des JO s'est d\u00e9roul\u00e9 le 21 octobre 2019 au Grand Rex. Ce nouvel embl\u00e8me et cette nouvelle typographie ont \u00e9t\u00e9 con\u00e7us par le designer Sylvain Boyer avec les agences Royalties & Ecobranding. Rond, il rassemble trois symboles : une m\u00e9daille d'or, la flamme olympique et Marianne, symbolis\u00e9e par un visage de femme mais priv\u00e9e de son bonnet phrygien caract\u00e9ristique. La typographie dessin\u00e9e fait r\u00e9f\u00e9rence \u00e0 l'Art d\u00e9co, mouvement artistique des ann\u00e9es 1920, d\u00e9cennie pendant laquelle ont eu lieu pour la derni\u00e8re fois les Jeux olympiques \u00e0 Paris en 1924. Pour la premi\u00e8re fois, ce logo sera unique pour les Jeux olympiques et les Jeux paralympiques."}], "pipeline_tag": "token-classification", "co2_eq_emissions": 80, "model-index": [{"name": "NERmembert-large-4entities", "results": []}]}
transformers safetensors camembert token-classification fr dataset:CATIE-AQ/frenchNER_4entities arxiv:1910.09700 base_model:camembert/camembert-large doi:10.57967/hf/1752 license:cc-by-4.0 co2_eq_emissions autotrain_compatible endpoints_compatible has_space region:us