transformers pytorch mt5 text2text-generation summarization arabic ar en Abstractive Summarization generated_from_trainer dataset:xlsum license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- xlsum
license: apache-2.0
tags:
- summarization
- arabic
- ar
- en
- mt5
- Abstractive Summarization
- generated_from_trainer
model-index:
- name: mt5-base-finetuned-english-finetuned-english-arabic
  results: []
---

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# mt5-base-finetuned-english-finetuned-english-arabic

This model is a fine-tuned version of [eslamxm/mt5-base-finetuned-english](https://huggingface.co/eslamxm/mt5-base-finetuned-english) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4788
- Rouge-1: 22.55
- Rouge-2: 9.84
- Rouge-l: 20.5
- Gen Len: 19.0
- Bertscore: 71.39

## 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
- num_epochs: 5
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.999         | 1.0   | 1172 | 3.9343          | 17.67   | 5.93    | 15.86   | 19.0    | 69.69     |
| 4.008         | 2.0   | 2344 | 3.6655          | 19.48   | 7.67    | 17.67   | 19.0    | 70.49     |
| 3.7463        | 3.0   | 3516 | 3.5503          | 20.47   | 8.24    | 18.6    | 19.0    | 70.86     |
| 3.5924        | 4.0   | 4688 | 3.4942          | 20.95   | 8.45    | 19.05   | 19.0    | 71.0      |
| 3.4979        | 5.0   | 5860 | 3.4788          | 21.34   | 8.75    | 19.39   | 19.0    | 71.11     |


### Framework versions

- Transformers 4.19.0
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
