transformers pytorch tensorboard encoder-decoder text2text-generation summarization ar mbert Abstractive Summarization generated_from_trainer dataset:xlsum autotrain_compatible endpoints_compatible region:us
---
datasets:
- xlsum
tags:
- summarization
- ar
- encoder-decoder
- mbert
- Abstractive Summarization
- generated_from_trainer
model-index:
- name: mbert2mbert-finetuned-ar-xlsum
  results: []
---

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# mbert2mbert-finetuned-ar-xlsum

This model is a fine-tuned version of [](https://huggingface.co/) on the xlsum dataset.

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 8
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
