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