Update README.md

README.md

@@ -15,9 +15,9 @@ pipeline_tag: visual-question-answering
 
 **paligemma-3b-ft-docvqa-896-lora** is a fine-tuned version of the **[google/paligemma-3b-ft-docvqa-896](https://huggingface.co/google/paligemma-3b-ft-docvqa-896)** model, specifically trained on the **[doc-vqa](https://huggingface.co/datasets/cmarkea/doc-vqa)** dataset published by Crédit Mutuel Arkéa. Optimized using the **LoRA** (Low-Rank Adaptation) method, this model was designed to enhance performance while reducing the complexity of fine-tuning.
 
-During training, particular attention was given to linguistic balance, with a focus on French. The model was exposed to a predominantly French context, with a 70% likelihood of interacting with French questions/answers for a given image. It operates exclusively in bfloat16 precision, optimizing computational resources. The entire training process took 3 week on a single A100 40GB.
+During training, particular attention was given to linguistic balance, with a focus on french. The model was exposed to a predominantly french context, with a 70% likelihood of interacting with french questions/answers for a given image. It operates exclusively in bfloat16 precision, optimizing computational resources. The entire training process took 3 week on a single A100 40GB.
 
-Thanks to its multilingual specialization and emphasis on French, this model excels in francophone environments, while also performing well in English. It is especially suited for tasks that require the analysis and understanding of complex documents, such as extracting information from forms, invoices, reports, and other text-based documents in a visual question-answering context.
+Thanks to its multilingual specialization and emphasis on french, this model excels in francophone environments, while also performing well in english. It is especially suited for tasks that require the analysis and understanding of complex documents, such as extracting information from forms, invoices, reports, and other text-based documents in a visual question-answering context.
 
 ## Model Details