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Update ModelCard
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README.md
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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
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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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## Intended uses & limitations
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## Training and evaluation data
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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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Named Entity Recognition (NER) tasks specifically for French.
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Token classification tasks involving French text.
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### Limitations:
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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
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