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---
tags:
- summarization
- ar
- seq2seq
- mbart
- Abstractive Summarization
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: mbart-finetune-ar-xlsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart-finetune-ar-xlsum
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4328
- Rouge-1: 15.56
- Rouge-2: 4.64
- Rouge-l: 13.59
- Gen Len: 38.86
- Bertscore: 71.53
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 5
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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