transformers pytorch t5 text2text-generation summarization Arat5v2 abstractive summarization ar xlsum generated_from_trainer dataset:xlsum base_model:UBC-NLP/AraT5v2-base-1024 base_model:finetune:UBC-NLP/AraT5v2-base-1024 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: UBC-NLP/AraT5v2-base-1024
datasets:
- xlsum
tags:
- summarization
- Arat5v2
- abstractive summarization
- ar
- xlsum
- generated_from_trainer
model-index:
- name: AraT5v2-base-1024-finetune-ar-xlsum
  results: []
---

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# AraT5v2-base-1024-finetune-ar-xlsum

This model is a fine-tuned version of [UBC-NLP/AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7983
- Rouge-1: 33.4
- Rouge-2: 16.14
- Rouge-l: 29.31
- Gen Len: 18.63
- Bertscore: 74.57

## 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: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 6.1614        | 1.0   | 195  | 3.9898          | 28.51   | 12.02   | 24.64   | 18.87   | 72.64     |
| 4.5342        | 2.0   | 390  | 3.9048          | 29.5    | 13.01   | 25.85   | 18.53   | 73.34     |
| 4.2029        | 3.0   | 585  | 3.8162          | 31.64   | 14.33   | 27.54   | 18.57   | 73.88     |
| 3.9689        | 4.0   | 781  | 3.7949          | 31.87   | 14.56   | 27.9    | 18.55   | 74.04     |
| 3.8278        | 5.0   | 976  | 3.7702          | 31.85   | 14.58   | 27.74   | 18.74   | 73.96     |
| 3.6921        | 6.0   | 1171 | 3.7775          | 32.27   | 14.95   | 28.16   | 18.78   | 74.23     |
| 3.5632        | 7.0   | 1367 | 3.7751          | 32.54   | 15.04   | 28.4    | 18.72   | 74.36     |
| 3.493         | 8.0   | 1562 | 3.7815          | 32.35   | 14.95   | 28.24   | 18.71   | 74.32     |
| 3.4189        | 9.0   | 1757 | 3.7908          | 32.39   | 14.99   | 28.32   | 18.73   | 74.32     |
| 3.3492        | 9.98  | 1950 | 3.7983          | 32.6    | 15.19   | 28.5    | 18.72   | 74.35     |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
