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  This model is based on the [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) architecture, renowned for its effectiveness in translation and text generation tasks across numerous languages.
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  ## Training Data
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- The model was fine-tuned on a subset of 96,780 sentences from the Ubertext dataset, focusing on news content. The verbalized equivalents were created using Google Gemini Pro, providing a rich basis for learning text transformation tasks.
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  Dataset [skypro1111/ubertext-2-news-verbalized](https://huggingface.co/datasets/skypro1111/ubertext-2-news-verbalized)
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  ## Training Procedure
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- The model underwent 70,000 training steps, which is almost 2 epochs, with further training the results degraded.
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  ```python
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  from transformers import MBartForConditionalGeneration, AutoTokenizer, Trainer, TrainingArguments
 
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  This model is based on the [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) architecture, renowned for its effectiveness in translation and text generation tasks across numerous languages.
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  ## Training Data
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+ The model was fine-tuned on a subset of 457,610 sentences from the Ubertext dataset, focusing on news content. The verbalized equivalents were created using Google Gemini Pro, providing a rich basis for learning text transformation tasks.
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  Dataset [skypro1111/ubertext-2-news-verbalized](https://huggingface.co/datasets/skypro1111/ubertext-2-news-verbalized)
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  ## Training Procedure
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+ The model underwent 410,000 training steps (1 epoch).
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  ```python
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  from transformers import MBartForConditionalGeneration, AutoTokenizer, Trainer, TrainingArguments