transformers safetensors roberta token-classification fr dataset:CATIE-AQ/frenchNER_4entities arxiv:1910.09700 arxiv:2411.08868 base_model:almanach/camembertv2-base base_model:finetune:almanach/camembertv2-base license:mit co2_eq_emissions autotrain_compatible endpoints_compatible region:us
---
base_model: almanach/camembertv2-base
datasets:
- CATIE-AQ/frenchNER_4entities
language:
- fr
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
pipeline_tag: token-classification
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.'
co2_eq_emissions: 25.5
model-index:
- name: NERmembert2-4entities
  results: []
---


# NERmembert2-4entities

## Model Description

We present **NERmembert2-4entities**, which is a [CamemBERT v2 base](https://huggingface.co/almanach/camembertv2-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/).


## 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>Parameters</th>
      <th><br>Context</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>110M</td>
        <td><br>512 tokens</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>67.5M</td>
        <td><br>512 tokens</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-4entities">NERmembert-base-4entities</a></td>
        <td><br>110M</td>
        <td><br>512 tokens</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>NERmembert2-4entities (this model)</td>
        <td><br>111M</td>
        <td><br>1024 tokens</td>
        <td><br>0.978</td>
        <td><br>0.958</td>
        <td><br>0.901</td>
        <td><br>0.806</td>
    </tr>
    <tr>
        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-4entities">NERmemberta-4entities</a></td>
        <td><br>111M</td>
        <td><br>1024 tokens</td>
        <td><br>0.979</td>
        <td><br>0.961</td>
        <td><br>0.915</td>
        <td><br>0.812</td>
    </tr>
      <tr>
        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td>
        <td><br>336M</td>
        <td><br>512 tokens</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 (110M)</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 (67.5M)</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-4entities">NERmembert-base-4entities (110M)</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><a href="https://hf.co/CATIE-AQ/NERmembert2-4entities">NERmembert2-4entities (111M) (this model)</a></td>
        <td><br>Precision</td>
        <td><br>0.973</td>
        <td><br>0.951</td>
        <td><br>0.882</td>
        <td><br>0.860</td>
        <td><br>0.991</td>
        <td><br>0.982</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.982</td>
        <td><br>0.965</td>
        <td><br>0.921</td>
        <td><br>0.759</td>
        <td><br>0.994</td>
        <td><br>0.982</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.978</td>
        <td><br>0.958</td>
        <td><br>0.901</td>
        <td><br>0.806</td>
        <td><br>0.992</td>
        <td><br>0.982</td>
    </tr>
    <tr>
        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-4entities">NERmemberta-4entities (111M)</a></td>
        <td><br>Precision</td>
        <td><br>0.976</td>
        <td><br>0.955</td>
        <td><br>0.894</td>
        <td><br>0.856</td>
        <td><br>0.991</td>
        <td><br>0.983</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.983</td>
        <td><br>0.968</td>
        <td><br>0.936</td>
        <td><br>0.772</td>
        <td><br>0.994</td>
        <td><br>0.983</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.979</td>
        <td><br>0.961</td>
        <td><br>0.915</td>
        <td><br>0.812</td>
        <td><br>0.992</td>
        <td><br>0.983</td>
    </tr>
      <tr>
        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities (336M)</a></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 (110M)</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 (67.5M)</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-4entities">NERmembert-base-4entities (110M)</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>NERmembert2-4entities (111M) (this model)</td>
        <td><br>0.964</td>
        <td><br>0.888</td>
        <td><br>0.864</td>
        <td><br>0.850</td>
    </tr>
    <tr>
        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-4entities">NERmemberta-4entities (111M)</a></td>
        <td><br>0.966</td>
        <td><br>0.891</td>
        <td><br>0.867</td>
        <td><br>0.862</td>
    </tr>
      <tr>
        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities (336M)</a></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 (110M)</a></td>
        <td><br>Precision</td>
        <td><br>0.908</td>
        <td><br>0.717</td>
        <td><br>0.753</td>
        <td><br>0.620</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 (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>0.589</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-4entities">NERmembert-base-4entities (110M)</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><a href="https://hf.co/CATIE-AQ/NERmembert2-4entities">NERmembert2-4entities (111M) (this model)</a></td>
        <td><br>Precision</td>
        <td><br>0.953</td>
        <td><br>0.890</td>
        <td><br>0.870</td>
        <td><br>0.842</td>
        <td><br>0.976</td>
        <td><br>0.952</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.975</td>
        <td><br>0.887</td>
        <td><br>0.857</td>
        <td><br>0.858</td>
        <td><br>0.970</td>
        <td><br>0.952</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.964</td>
        <td><br>0.888</td>
        <td><br>0.864</td>
        <td><br>0.850</td>
        <td><br>0.973</td>
        <td><br>0.952</td>
    </tr>
    <tr>
        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-4entities">NERmemberta-4entities (111M)</a></td>
        <td><br>Precision</td>
        <td><br>0.961</td>
        <td><br>0.895</td>
        <td><br>0.859</td>
        <td><br>0.845</td>
        <td><br>0.978</td>
        <td><br>0.953</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.972</td>
        <td><br>0.886</td>
        <td><br>0.876</td>
        <td><br>0.879</td>
        <td><br>0.970</td>
        <td><br>0.953</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.966</td>
        <td><br>0.891</td>
        <td><br>0.867</td>
        <td><br>0.862</td>
        <td><br>0.974</td>
        <td><br>0.953</td>
    </tr>
        <tr>
        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities (336M)</a></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 (110M)</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 (67.5M)</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-4entities">NERmembert-base-4entities (110M)</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>NERmembert2-4entities (111M) (this model)</td>
        <td><br>0.986</td>
        <td><br>0.974</td>
        <td><br>0.937</td>
        <td><br>0.761</td>
    </tr>
    <tr>
        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-4entities">NERmemberta-4entities (111M)</a></td>
        <td><br><b>0.987</b></td>
        <td><br><b>0.976</b></td>
        <td><br>0.942</td>
        <td><br>0.770</td>
    </tr>
      <tr>
        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities (336M)</a></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 (110M)</a></td>
        <td><br>Precision</td>
        <td><br>0.931</td>
        <td><br>0.893</td>
        <td><br>0.827</td>
        <td><br>0.725</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 (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>0.705</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-4entities">NERmembert-base-4entities (110M)</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><a href="https://hf.co/CATIE-AQ/NERmembert2-4entities">NERmembert2-4entities (111M) (this model)</a></td>
        <td><br>Precision</td>
        <td><br>0.976</td>
        <td><br>0.962</td>
        <td><br>0.903</td>
        <td><br>0.846</td>
        <td><br>0.988</td>
        <td><br>0.980</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.995</td>
        <td><br>0.986</td>
        <td><br>0.974</td>
        <td><br>0.692</td>
        <td><br>0.992</td>
        <td><br>0.980</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.986</td>
        <td><br>0.974</td>
        <td><br>0.937</td>
        <td><br>0.761</td>
        <td><br>0.990</td>
        <td><br>0.980</td>
    </tr>
    <tr>
        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-4entities">NERmemberta-4entities (111M)</a></td>
        <td><br>Precision</td>
        <td><br>0.979</td>
        <td><br>0.963</td>
        <td><br>0.912</td>
        <td><br>0.848</td>
        <td><br>0.988</td>
        <td><br>0.981</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.996</td>
        <td><br>0.989</td>
        <td><br>0.975</td>
        <td><br>0.705</td>
        <td><br>0.992</td>
        <td><br>0.981</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.987</td>
        <td><br>0.976</td>
        <td><br>0.942</td>
        <td><br>0.770</td>
        <td><br>0.990</td>
        <td><br>0.981</td>
    </tr>
        <tr>
        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities (336M)</a></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 (110M)</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 (67.5M)</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-4entities">NERmembert-base-4entities (110M)</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>NERmembert2-4entities (111M) (this model)</td>
        <td><br>0.968</td>
        <td><br>0.945</td>
        <td><br>0.874</td>
        <td><br>0.871</td>
    </tr>
    <tr>
        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-4entities">NERmemberta-4entities (111M)</a></td>
        <td><br>0.969</td>
        <td><br>0.950</td>
        <td><br>0.897</td>
        <td><br>0.871</td>
    </tr>
      <tr>
        <td rowspan="1"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities (336M)</a></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 (110M)</a></td>
        <td><br>Precision</td>
        <td><br>0.986</td>
        <td><br>0.962</td>
        <td><br>0.925</td>
        <td><br>0.943</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 (67.5M)</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-4entities">NERmembert-base-4entities (110M)</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><a href="https://hf.co/CATIE-AQ/NERmembert2-4entities">NERmembert2-4entities (111M) (this model)</a></td>
        <td><br>Precision</td>
        <td><br>0.970</td>
        <td><br>0.942</td>
        <td><br>0.865</td>
        <td><br>0.883</td>
        <td><br>0.996</td>
        <td><br>0.985</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.966</td>
        <td><br>0.948</td>
        <td><br>0.883</td>
        <td><br>0.859</td>
        <td><br>0.996</td>
        <td><br>0.985</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.968</td>
        <td><br>0.945</td>
        <td><br>0.874</td>
        <td><br>0.871</td>
        <td><br>0.996</td>
        <td><br>0.985</td>
    </tr>
    <tr>
        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmemberta-4entities">NERmemberta-4entities (111M)</a></td>
        <td><br>Precision</td>
        <td><br>0.974</td>
        <td><br>0.949</td>
        <td><br>0.883</td>
        <td><br>0.869</td>
        <td><br>0.996</td>
        <td><br>0.986</td>
    </tr>
    <tr>
        <td><br>Recall</td>
        <td><br>0.965</td>
        <td><br>0.951</td>
        <td><br>0.910</td>
        <td><br>0.872</td>
        <td><br>0.996</td>
        <td><br>0.986</td>
    </tr>
    <tr>
        <td>F1</td>
        <td><br>0.969</td>
        <td><br>0.950</td>
        <td><br>0.897</td>
        <td><br>0.871</td>
        <td><br>0.996</td>
        <td><br>0.986</td>
    </tr>
        <tr>
        <td rowspan="3"><br><a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities (336M)</a></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/NERmembert2-4entities', tokenizer='CATIE-AQ/NERmembert2-4entities', 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)
```


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


## 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:** 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)*: 0.0255 kg eq. CO2



## Citations

### NERmemBERT2-4entities
```
@misc {NERmemberta2024,
    author       = { {BOURDOIS, Loïck} },  
    organization = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { NERmemberta-4entities },
	year         = 2024,
	url          = { https://huggingface.co/CATIE-AQ/NERmemberta-4entities },
	doi          = { 10.57967/hf/3640 },
	publisher    = { Hugging Face }
}
```

### 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-4entities },
	doi          = { 10.57967/hf/1752 },
	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}}
```

### 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 }}
```

### 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 }  
}
```

## License
MIT