---
license: apache-2.0
tags:
- summarization
- arabic
- am
- es
- amharic
- mt5
- Abstractive Summarization
- generated_from_trainer
model-index:
- name: mt5-base-finetuned-ar-sp
  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. -->

# mt5-base-finetuned-ar-sp

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co./google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2772
- Rouge-1: 23.01
- Rouge-2: 10.41
- Rouge-l: 20.94
- Gen Len: 19.0
- Bertscore: 71.56

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 4.1968        | 1.0   | 1352 | 3.5142          | 18.69   | 6.73    | 16.97   | 19.0    | 70.3      |
| 3.6932        | 2.0   | 2704 | 3.3799          | 20.67   | 8.38    | 18.75   | 19.0    | 70.82     |
| 3.5058        | 3.0   | 4056 | 3.3184          | 20.97   | 8.58    | 19.08   | 19.0    | 71.08     |
| 3.3832        | 4.0   | 5408 | 3.2851          | 21.59   | 8.94    | 19.63   | 19.0    | 71.28     |
| 3.2994        | 5.0   | 6760 | 3.2772          | 21.84   | 9.23    | 19.85   | 19.0    | 71.34     |


### Framework versions

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