NERmembert-base-4entities

CATIE-AQ/NERmembert-base-4entities

NERmembert-base-4entities is a token classification model from CATIE-AQ. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

We present NERmembert-base-4entities, which is a 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 datas…

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Task: Token Classification
Library: transformers
License: mit
Model type: camembert
Access: Public
Created: Jan 4, 2024
Updated: Nov 26, 2024
SHA: 6e9fde60

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From the Hugging Face model README

NERmembert-base-4entities

Model Description

We present NERmembert-base-4entities, which is a 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.
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 or French.

Dataset

The dataset used is 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> Splits</th> <th> O</th> <th> PER</th> <th> LOC</th> <th> ORG</th> <th> MISC</th> </tr> </thead> <tbody> <td> train</td> <td> 7,539,692</td> <td> 307,144</td> <td> 286,746</td> <td> 127,089</td> <td> 799,494</td> </tr> <tr> <td> validation</td> <td> 544,580</td> <td> 24,034</td> <td> 21,585</td> <td> 5,927</td> <td> 18,221</td> </tr> <tr> <td> test</td> <td> 720,623</td> <td> 32,870</td> <td> 29,683</td> <td> 7,911</td> <td> 21,760</td> </tr> </tbody> </table>

Evaluation results

The evaluation was carried out using the 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> Model</th> <th> PER</th> <th> LOC</th> <th> ORG</th> <th> MISC</th> </tr> </thead> <tbody> <tr> <td rowspan="1"> <a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td> <td> 0.971</td> <td> 0.947</td> <td> 0.902</td> <td> 0.663</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td> <td> 0.974</td> <td> 0.948</td> <td> 0.892</td> <td> 0.658</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td> <td> 0.978</td> <td> 0.957</td> <td> 0.904</td> <td> 0</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td> <td> 0.980</td> <td> 0.963</td> <td> 0.919</td> <td> 0</td> </tr> <tr> <td rowspan="1"> NERmembert-base-4entities (this model)</td> <td> 0.978</td> <td> 0.958</td> <td> 0.903</td> <td> 0.814</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td> <td> 0.982</td> <td> 0.964</td> <td> 0.919</td> <td> 0.834</td> </tr> </tbody> </table> <details> <summary>Full results</summary> <table> <thead> <tr> <th> Model</th> <th> Metrics</th> <th> PER</th> <th> LOC</th> <th> ORG</th> <th> MISC</th> <th> O</th> <th> Overall</th> </tr> </thead> <tbody> <tr> <td rowspan="3"> <a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td> <td> Precision</td> <td> 0.952</td> <td> 0.924</td> <td> 0.870</td> <td> 0.845</td> <td> 0.986</td> <td> 0.976</td> </tr> <tr> <td> Recall</td> <td> 0.990</td> <td> 0.972</td> <td> 0.938</td> <td> 0.546</td> <td> 0.992</td> <td> 0.976</td> </tr> <tr> <td>F1</td> <td> 0.971</td> <td> 0.947</td> <td> 0.902</td> <td> 0.663</td> <td> 0.989</td> <td> 0.976</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td> <td> Precision</td> <td> 0.962</td> <td> 0.933</td> <td> 0.857</td> <td> 0.830</td> <td> 0.985</td> <td> 0.976</td> </tr> <tr> <td> Recall</td> <td> 0.987</td> <td> 0.963</td> <td> 0.930</td> <td> 0.545</td> <td> 0.993</td> <td> 0.976</td> </tr> <tr> <td>F1</td> <td> 0.974</td> <td> 0.948</td> <td> 0.892</td> <td> 0.658</td> <td> 0.989</td> <td> 0.976</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td> <td> Precision</td> <td> 0.973</td> <td> 0.955</td> <td> 0.886</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td> Recall</td> <td> 0.983</td> <td> 0.960</td> <td> 0.923</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td>F1</td> <td> 0.978</td> <td> 0.957</td> <td> 0.904</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td> <td> Precision</td> <td> 0.978</td> <td> 0.960</td> <td> 0.899</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td> Recall</td> <td> 0.985</td> <td> 0.966</td> <td> 0.940</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td>F1</td> <td> 0.980</td> <td> 0.963</td> <td> 0.919</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td rowspan="3"> NERmembert-base-4entities (this model)</td> <td> Precision</td> <td> 0.973</td> <td> 0.951</td> <td> 0.888</td> <td> 0.850</td> <td> 0.993</td> <td> 0.984</td> </tr> <tr> <td> Recall</td> <td> 0.983</td> <td> 0.964</td> <td> 0.918</td> <td> 0.781</td> <td> 0.993</td> <td> 0.984</td> </tr> <tr> <td>F1</td> <td> 0.978</td> <td> 0.958</td> <td> 0.903</td> <td> 0.814</td> <td> 0.993</td> <td> 0.984</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td> <td> Precision</td> <td> 0.977</td> <td> 0.961</td> <td> 0.896</td> <td> 0.872</td> <td> 0.993</td> <td> 0.986</td> </tr> <tr> <td> Recall</td> <td> 0.987</td> <td> 0.966</td> <td> 0.943</td> <td> 0.798</td> <td> 0.995</td> <td> 0.986</td> </tr> <tr> <td>F1</td> <td> 0.982</td> <td> 0.964</td> <td> 0.919</td> <td> 0.834</td> <td> 0.994</td> <td> 0.986</td> </tr> </tbody> </table> </details>

In detail:

multiconer

For space reasons, we show only the F1 of the different models. You can see the full results below the table.

<table> <thead> <tr> <th> Model</th> <th> PER</th> <th> LOC</th> <th> ORG</th> <th> MISC</th> </tr> </thead> <tbody> <tr> <td rowspan="1"> <a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td> <td> 0.940</td> <td> 0.761</td> <td> 0.723</td> <td> 0.560</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td> <td> 0.921</td> <td> 0.748</td> <td> 0.694</td> <td> 0.530</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td> <td> 0.960</td> <td> 0.887</td> <td> 0.877</td> <td> 0</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td> <td> 0.965</td> <td> 0.902</td> <td> 0.896</td> <td> 0</td> </tr> <tr> <td rowspan="1"> NERmembert-base-4entities (this model)</td> <td> 0.960</td> <td> 0.890</td> <td> 0.867</td> <td> 0.852</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td> <td> 0.969</td> <td> 0.919</td> <td> 0.904</td> <td> 0.864</td> </tr> </tbody> </table> <details> <summary>Full results</summary> <table> <thead> <tr> <th> Model</th> <th> Metrics</th> <th> PER</th> <th> LOC</th> <th> ORG</th> <th> MISC</th> <th> O</th> <th> Overall</th> </tr> </thead> <tbody> <tr> <td rowspan="3"> <a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td> <td> Precision</td> <td> 0.908</td> <td> 0.717</td> <td> 0.753</td> <td> 0.620</td> <td> 0.936</td> <td> 0.889</td> </tr> <tr> <td> Recall</td> <td> 0.975</td> <td> 0.811</td> <td> 0.696</td> <td> 0.511</td> <td> 0.938</td> <td> 0.889</td> </tr> <tr> <td>F1</td> <td> 0.940</td> <td> 0.761</td> <td> 0.723</td> <td> 0.560</td> <td> 0.937</td> <td> 0.889</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td> <td> Precision</td> <td> 0.885</td> <td> 0.738</td> <td> 0.737</td> <td> 0.589</td> <td> 0.928</td> <td> 0.881</td> </tr> <tr> <td> Recall</td> <td> 0.960</td> <td> 0.759</td> <td> 0.655</td> <td> 0.482</td> <td> 0.939</td> <td> 0.881</td> </tr> <tr> <td>F1</td> <td> 0.921</td> <td> 0.748</td> <td> 0.694</td> <td> 0.530</td> <td> 0.934</td> <td> 0.881</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td> <td> Precision</td> <td> 0.957</td> <td> 0.894</td> <td> 0.876</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td> Recall</td> <td> 0.962</td> <td> 0.880</td> <td> 0.878</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td>F1</td> <td> 0.960</td> <td> 0.887</td> <td> 0.877</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td> <td> Precision</td> <td> 0.960</td> <td> 0.903</td> <td> 0.916</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td> Recall</td> <td> 0.970</td> <td> 0.900</td> <td> 0.877</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td>F1</td> <td> 0.965</td> <td> 0.902</td> <td> 0.896</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td rowspan="3"> NERmembert-base-4entities (this model)</td> <td> Precision</td> <td> 0.954</td> <td> 0.893</td> <td> 0.851</td> <td> 0.849</td> <td> 0.979</td> <td> 0.954</td> </tr> <tr> <td> Recall</td> <td> 0.967</td> <td> 0.887</td> <td> 0.883</td> <td> 0.855</td> <td> 0.974</td> <td> 0.954</td> </tr> <tr> <td>F1</td> <td> 0.960</td> <td> 0.890</td> <td> 0.867</td> <td> 0.852</td> <td> 0.977</td> <td> 0.954</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td> <td> Precision</td> <td> 0.964</td> <td> 0.922</td> <td> 0.904</td> <td> 0.856</td> <td> 0.981</td> <td> 0.961</td> </tr> <tr> <td> Recall</td> <td> 0.975</td> <td> 0.917</td> <td> 0.904</td> <td> 0.872</td> <td> 0.976</td> <td> 0.961</td> </tr> <tr> <td>F1</td> <td> 0.969</td> <td> 0.919</td> <td> 0.904</td> <td> 0.864</td> <td> 0.978</td> <td> 0.961</td> </tr> </tbody> </table> </details>

multinerd

For space reasons, we show only the F1 of the different models. You can see the full results below the table.

<table> <thead> <tr> <th> Model</th> <th> PER</th> <th> LOC</th> <th> ORG</th> <th> MISC</th> </tr> </thead> <tbody> <tr> <td rowspan="1"> <a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td> <td> 0.962</td> <td> 0.934</td> <td> 0.888</td> <td> 0.419</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td> <td> 0.972</td> <td> 0.938</td> <td> 0.884</td> <td> 0.430</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td> <td> 0.985</td> <td> 0.973</td> <td> 0.938</td> <td> 0</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td> <td> 0.987</td> <td> 0.979</td> <td> 0.953</td> <td> 0</td> </tr> <tr> <td rowspan="1"> NERmembert-base-4entities (this model)</td> <td> 0.985</td> <td> 0.973</td> <td> 0.938</td> <td> 0.770</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td> <td> 0.987</td> <td> 0.976</td> <td> 0.948</td> <td> 0.790</td> </tr> </tbody> </table> <details> <summary>Full results</summary> <table> <thead> <tr> <th> Model</th> <th> Metrics</th> <th> PER</th> <th> LOC</th> <th> ORG</th> <th> MISC</th> <th> O</th> <th> Overall</th> </tr> </thead> <tbody> <tr> <td rowspan="3"> <a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td> <td> Precision</td> <td> 0.931</td> <td> 0.893</td> <td> 0.827</td> <td> 0.725</td> <td> 0.979</td> <td> 0.966</td> </tr> <tr> <td> Recall</td> <td> 0.994</td> <td> 0.980</td> <td> 0.959</td> <td> 0.295</td> <td> 0.990</td> <td> 0.966</td> </tr> <tr> <td>F1</td> <td> 0.962</td> <td> 0.934</td> <td> 0.888</td> <td> 0.419</td> <td> 0.984</td> <td> 0.966</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td> <td> Precision</td> <td> 0.954</td> <td> 0.908</td> <td> 0.817</td> <td> 0.705</td> <td> 0.977</td> <td> 0.967</td> </tr> <tr> <td> Recall</td> <td> 0.991</td> <td> 0.969</td> <td> 0.963</td> <td> 0.310</td> <td> 0.990</td> <td> 0.967</td> </tr> <tr> <td>F1</td> <td> 0.972</td> <td> 0.938</td> <td> 0.884</td> <td> 0.430</td> <td> 0.984</td> <td> 0.967</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td> <td> Precision</td> <td> 0.974</td> <td> 0.965</td> <td> 0.910</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td> Recall</td> <td> 0.995</td> <td> 0.981</td> <td> 0.968</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td>F1</td> <td> 0.985</td> <td> 0.973</td> <td> 0.938</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td> <td> Precision</td> <td> 0.979</td> <td> 0.970</td> <td> 0.927</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td> Recall</td> <td> 0.996</td> <td> 0.987</td> <td> 0.980</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td>F1</td> <td> 0.987</td> <td> 0.979</td> <td> 0.953</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td rowspan="3"> NERmembert-base-4entities (this model)</td> <td> Precision</td> <td> 0.976</td> <td> 0.961</td> <td> 0.911</td> <td> 0.829</td> <td> 0.991</td> <td> 0.983</td> </tr> <tr> <td> Recall</td> <td> 0.994</td> <td> 0.985</td> <td> 0.967</td> <td> 0.719</td> <td> 0.993</td> <td> 0.983</td> </tr> <tr> <td>F1</td> <td> 0.985</td> <td> 0.973</td> <td> 0.938</td> <td> 0.770</td> <td> 0.992</td> <td> 0.983</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td> <td> Precision</td> <td> 0.979</td> <td> 0.967</td> <td> 0.922</td> <td> 0.852</td> <td> 0.991</td> <td> 0.985</td> </tr> <tr> <td> Recall</td> <td> 0.996</td> <td> 0.986</td> <td> 0.974</td> <td> 0.736</td> <td> 0.994</td> <td> 0.985</td> </tr> <tr> <td>F1</td> <td> 0.987</td> <td> 0.976</td> <td> 0.948</td> <td> 0.790</td> <td> 0.993</td> <td> 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> Model</th> <th> PER</th> <th> LOC</th> <th> ORG</th> <th> MISC</th> </tr> </thead> <tbody> <tr> <td rowspan="1"> <a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td> <td> 0.986</td> <td> 0.966</td> <td> 0.938</td> <td> 0.938</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td> <td> 0.983</td> <td> 0.964</td> <td> 0.925</td> <td> 0.926</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td> <td> 0.970</td> <td> 0.945</td> <td> 0.878</td> <td> 0</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td> <td> 0.972</td> <td> 0.953</td> <td> 0.893</td> <td> 0</td> </tr> <tr> <td rowspan="1"> NERmembert-base-4entities (this model)</td> <td> 0.970</td> <td> 0.945</td> <td> 0.876</td> <td> 0.872</td> </tr> <tr> <td rowspan="1"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td> <td> 0.975</td> <td> 0.953</td> <td> 0.896</td> <td> 0.893</td> </tr> </tbody> </table> <details> <summary>Full results</summary> <table> <thead> <tr> <th> Model</th> <th> Metrics</th> <th> PER</th> <th> LOC</th> <th> ORG</th> <th> MISC</th> <th> O</th> <th> Overall</th> </tr> </thead> <tbody> <tr> <td rowspan="3"> <a href="https://hf.co/Jean-Baptiste/camembert-ner">Jean-Baptiste/camembert-ner</a></td> <td> Precision</td> <td> 0.986</td> <td> 0.962</td> <td> 0.925</td> <td> 0.943</td> <td> 0.998</td> <td> 0.992</td> </tr> <tr> <td> Recall</td> <td> 0.987</td> <td> 0.969</td> <td> 0.951</td> <td> 0.933</td> <td> 0.997</td> <td> 0.992</td> </tr> <tr> <td>F1</td> <td> 0.986</td> <td> 0.966</td> <td> 0.938</td> <td> 0.938</td> <td> 0.998</td> <td> 0.992</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/cmarkea/distilcamembert-base-ner">cmarkea/distilcamembert-base-ner</a></td> <td> Precision</td> <td> 0.982</td> <td> 0.964</td> <td> 0.910</td> <td> 0.942</td> <td> 0.997</td> <td> 0.991</td> </tr> <tr> <td> Recall</td> <td> 0.985</td> <td> 0.963</td> <td> 0.940</td> <td> 0.910</td> <td> 0.998</td> <td> 0.991</td> </tr> <tr> <td>F1</td> <td> 0.983</td> <td> 0.964</td> <td> 0.925</td> <td> 0.926</td> <td> 0.997</td> <td> 0.991</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-base-3entities">NERmembert-base-3entities</a></td> <td> Precision</td> <td> 0.971</td> <td> 0.947</td> <td> 0.866</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td> Recall</td> <td> 0.969</td> <td> 0.943</td> <td> 0.891</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td>F1</td> <td> 0.970</td> <td> 0.945</td> <td> 0.878</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-3entities">NERmembert-large-3entities</a></td> <td> Precision</td> <td> 0.973</td> <td> 0.953</td> <td> 0.873</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td> Recall</td> <td> 0.971</td> <td> 0.948</td> <td> 0.913</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td>F1</td> <td> 0.972</td> <td> 0.953</td> <td> 0.893</td> <td> 0</td> <td> X</td> <td> X</td> </tr> <tr> <td rowspan="3"> NERmembert-base-4entities (this model)</td> <td> Precision</td> <td> 0.970</td> <td> 0.944</td> <td> 0.872</td> <td> 0.878</td> <td> 0.996</td> <td> 0.986</td> </tr> <tr> <td> Recall</td> <td> 0.969</td> <td> 0.947</td> <td> 0.880</td> <td> 0.866</td> <td> 0.996</td> <td> 0.986</td> </tr> <tr> <td>F1</td> <td> 0.970</td> <td> 0.945</td> <td> 0.876</td> <td> 0.872</td> <td> 0.996</td> <td> 0.986</td> </tr> <tr> <td rowspan="3"> <a href="https://hf.co/CATIE-AQ/NERmembert-large-4entities">NERmembert-large-4entities</a></td> <td> Precision</td> <td> 0.975</td> <td> 0.957</td> <td> 0.872</td> <td> 0.901</td> <td> 0.997</td> <td> 0.989</td> </tr> <tr> <td> Recall</td> <td> 0.975</td> <td> 0.949</td> <td> 0.922</td> <td> 0.884</td> <td> 0.997</td> <td> 0.989</td> </tr> <tr> <td>F1</td> <td> 0.975</td> <td> 0.953</td> <td> 0.896</td> <td> 0.893</td> <td> 0.997</td> <td> 0.989</td> </tr> </tbody> </table> </details>

Usage

Code

from transformers import pipeline

ner = pipeline('token-classification', model='CATIE-AQ/NERmembert-base-4entities', tokenizer='CATIE-AQ/NERmembert-base-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(results)

[{'entity_group': 'MISC', 'score': 0.9456432, 'word': 'JO', 'start': 36, 'end': 38},
{'entity_group': 'LOC', 'score': 0.9990527, 'word': 'Grand Rex', 'start': 75, 'end': 84},
{'entity_group': 'PER', 'score': 0.99884754, 'word': 'Sylvain Boyer', 'start': 165, 'end': 178},
{'entity_group': 'ORG', 'score': 0.99118334, 'word': 'Royalties & Ecobranding', 'start': 196, 'end': 219},
{'entity_group': 'PER', 'score': 0.9446552, 'word': 'Marianne', 'start': 299, 'end': 307},
{'entity_group': 'MISC', 'score': 0.97599506, 'word': 'Art déco', 'start': 438, 'end': 446},
{'entity_group': 'MISC', 'score': 0.99798834, 'word': 'Jeux olympiques', 'start': 550, 'end': 565},
{'entity_group': 'LOC', 'score': 0.7205312, 'word': 'Paris', 'start': 568, 'end': 573},
{'entity_group': 'MISC', 'score': 0.996698, 'word': 'Jeux olympiques', 'start': 635, 'end': 650},
{'entity_group': 'MISC', 'score': 0.9955608, 'word': 'Jeux paralympiques', 'start': 658, 'end': 676}]

Try it through Space

A Space has been created to test the model. It is available here.

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
0.0407	1.0	41095	0.0547	0.9816	0.9816	0.9816	0.9816
0.0242	2.0	82190	0.0488	0.9843	0.9843	0.9843	0.9843
0.018	3.0	123285	0.0542	0.9844	0.9844	0.9844	0.9844

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 presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.

Hardware Type: A100 PCIe 40/80GB
Hours used: 1h45min
Cloud Provider: Private Infrastructure
Carbon Efficiency (kg/kWh): 0.046 (estimated from electricitymaps for the day of January 4, 2024.)
Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 0.01 kg eq. CO2

Citations

NERmembert-base-4entities

@misc {NERmembert2024,
    author       = { {BOURDOIS, Loïck} },  
    organization = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
	title        = { NERmembert-base-4entities },
	year         = 2024,
	url          = { https://huggingface.co/CATIE-AQ/NERmembert-base-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

MIT

Task: Token Classification
Library: transformers
Type: camembert
License: mit
Languages: fr
Created: Jan 4, 2024
Updated: Nov 26, 2024