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@@ -4,12 +4,24 @@ license: mit
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  base_model: camembert-base
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  tags:
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  - generated_from_trainer
 
 
 
 
 
 
 
 
 
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  metrics:
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  - accuracy
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  - f1
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  model-index:
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  - name: camembert-bank-post-classifier
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  results: []
 
 
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -18,6 +30,14 @@ should probably proofread and complete it, then remove this comment. -->
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  # camembert-bank-post-classifier
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  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
 
 
 
 
 
 
 
 
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  It achieves the following results on the evaluation set:
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  - Loss: 0.5783
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  - Accuracy: 0.8682
@@ -67,4 +87,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.56.1
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  - Pytorch 2.8.0+cu126
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  - Datasets 4.0.0
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- - Tokenizers 0.22.0
 
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  base_model: camembert-base
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  tags:
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  - generated_from_trainer
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+ - text-classification
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+ - french
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+ - banking
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+ - camembert
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+ - classification
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+ widget:
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+ - text: "Nous organisons une formation sur la gestion de projet"
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+ - text: "Lancement de notre nouvelle application mobile"
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+ - text: "Séminaire sur la responsabilité sociale d'entreprise"
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  metrics:
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  - accuracy
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  - f1
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  model-index:
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  - name: camembert-bank-post-classifier
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  results: []
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+ language:
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+ - fr
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+ pipeline_tag: text-classification
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  # camembert-bank-post-classifier
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  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
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+ It classifies posts on an internal social media in 7 categories :
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+ - Annonce RH & Vie de l'Entreprise
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+ - Communication de la Direction & Stratégie
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+ - Formation & Développement
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+ - Information & Alerte Pratique
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+ - Projet, Produit & Innovation
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+ - RSE & Engagement Citoyen
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+ - Événement Externe & Partenariat
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  It achieves the following results on the evaluation set:
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  - Loss: 0.5783
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  - Accuracy: 0.8682
 
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  - Transformers 4.56.1
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  - Pytorch 2.8.0+cu126
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  - Datasets 4.0.0
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+ - Tokenizers 0.22.0