---
tags:
- summarization
- ur
- seq2seq
- mbart
- Abstractive Summarization
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: MBart-finetuned-ur-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-finetuned-ur-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: 3.2663
- Rouge-1: 40.6
- Rouge-2: 18.9
- Rouge-l: 34.39
- Gen Len: 37.88
- Bertscore: 77.06

## 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: 5e-05
- 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