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Update ModelCard

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  1. README.md +18 -5
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@@ -6,6 +6,11 @@ tags:
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  model-index:
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  - name: xlm-roberta-base-finetuned-panx-fr
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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
@@ -13,22 +18,30 @@ should probably proofread and complete it, then remove this comment. -->
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  # xlm-roberta-base-finetuned-panx-fr
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- This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: 0.2750
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  - F1 Score: 0.8495
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  ## Model description
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- More information needed
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  ## Intended uses & limitations
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- More information needed
 
 
 
 
 
 
 
 
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  ## Training and evaluation data
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- More information needed
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  ## Training procedure
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  - Transformers 4.41.1
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  - Pytorch 2.3.0+cu121
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  - Datasets 2.19.1
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- - Tokenizers 0.19.1
 
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  model-index:
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  - name: xlm-roberta-base-finetuned-panx-fr
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  results: []
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+ language:
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+ - fr
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+ metrics:
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+ - f1
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+ library_name: transformers
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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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  # xlm-roberta-base-finetuned-panx-fr
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+ This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base).
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  It achieves the following results on the evaluation set:
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  - Loss: 0.2750
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  - F1 Score: 0.8495
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  ## Model description
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+ This model is a fine-tuned version of xlm-roberta-base on the French subset of the PAN-X dataset for Named Entity Recognition (NER). The model has been fine-tuned to perform token classification tasks and is evaluated on its performance in identifying named entities in French text.
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  ## Intended uses & limitations
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+ ### Intended uses:
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+
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+ Named Entity Recognition (NER) tasks specifically for French.
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+ Token classification tasks involving French text.
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+
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+ ### Limitations:
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+
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+ The model's performance is optimized for French and may not generalize well to other languages without further fine-tuning.
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+ The model's predictions are based on the data it was trained on and may not handle out-of-domain data as effectively.
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  ## Training and evaluation data
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+ The model was fine-tuned on the French subset of the PAN-X dataset, which includes labeled examples of named entities in French text. The evaluation data is a separate portion of the same dataset, used to assess the model's performance.
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  ## Training procedure
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  - Transformers 4.41.1
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  - Pytorch 2.3.0+cu121
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  - Datasets 2.19.1
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+ - Tokenizers 0.19.1