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--- |
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tags: |
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- summarization |
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language: |
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- it |
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metrics: |
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- rouge |
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model-index: |
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- name: summarization_mbart_mlsum |
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results: [] |
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datasets: |
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- ARTeLab/mlsum-it |
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--- |
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# mbart_summarization_mlsum |
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This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on mlsum-it for Abstractive Summarization. |
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It achieves the following results: |
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- Loss: 3.3336 |
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- Rouge1: 19.3489 |
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- Rouge2: 6.4028 |
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- Rougel: 16.3497 |
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- Rougelsum: 16.5387 |
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- Gen Len: 33.5945 |
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## Usage |
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```python |
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from transformers import MBartTokenizer, MBartForConditionalGeneration |
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tokenizer = MBartTokenizer.from_pretrained("ARTeLab/mbart-summarization-mlsum") |
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model = MBartForConditionalGeneration.from_pretrained("ARTeLab/mbart-summarization-mlsum") |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 4.0 |
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### Framework versions |
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- Transformers 4.15.0.dev0 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
