skypro1111
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Upload MBartForConditionalGeneration
Browse files- README.md +7 -6
- config.json +1 -1
- model.safetensors +1 -1
README.md
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---
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license: mit
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datasets:
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- skypro1111/ubertext-2-news-verbalized
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language:
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- uk
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---
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# Model Card for mbart-large-50-verbalization
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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
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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
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```python
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from transformers import MBartForConditionalGeneration, AutoTokenizer, Trainer, TrainingArguments
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---
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language:
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- uk
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license: mit
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library_name: transformers
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datasets:
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- skypro1111/ubertext-2-news-verbalized
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widget:
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- text: Очікувалось, що цей застосунок буде запущено о 11 ранку 22.08.2025, але розробники
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затягнули святкування і запуск був відкладений на 2 тижні.
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---
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# Model Card for mbart-large-50-verbalization
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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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config.json
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{
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"_name_or_path": "./
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"_num_labels": 3,
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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{
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"_name_or_path": "./results/facebook/mbart-large-50-verbalization/checkpoint-410000",
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"_num_labels": 3,
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"activation_dropout": 0.0,
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"activation_function": "gelu",
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model.safetensors
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