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---
tags:
- summarization
- Arat5-base
- abstractive summarization
- ar
- xlsum
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: AraT5-base-finetune-ar-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. -->
# AraT5-base-finetune-ar-xlsum
This model is a fine-tuned version of [UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4714
- Rouge-1: 29.55
- Rouge-2: 12.63
- Rouge-l: 25.8
- Gen Len: 18.76
- Bertscore: 73.3
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
| 11.9753 | 1.0 | 293 | 7.0887 | 11.93 | 2.56 | 10.93 | 17.19 | 63.85 |
| 6.7818 | 2.0 | 586 | 5.7712 | 19.94 | 6.34 | 17.65 | 18.64 | 69.0 |
| 5.9434 | 3.0 | 879 | 5.1083 | 23.51 | 8.56 | 20.66 | 18.88 | 70.78 |
| 5.451 | 4.0 | 1172 | 4.8538 | 25.84 | 10.05 | 22.63 | 18.42 | 72.04 |
| 5.1643 | 5.0 | 1465 | 4.6910 | 27.23 | 11.13 | 23.83 | 18.78 | 72.45 |
| 4.9693 | 6.0 | 1758 | 4.5950 | 28.42 | 11.71 | 24.82 | 18.74 | 72.94 |
| 4.8308 | 7.0 | 2051 | 4.5323 | 28.95 | 12.19 | 25.3 | 18.74 | 73.13 |
| 4.7284 | 8.0 | 2344 | 4.4956 | 29.19 | 12.37 | 25.53 | 18.76 | 73.18 |
| 4.653 | 9.0 | 2637 | 4.4757 | 29.44 | 12.48 | 25.63 | 18.78 | 73.23 |
| 4.606 | 10.0 | 2930 | 4.4714 | 29.55 | 12.63 | 25.8 | 18.76 | 73.3 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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