metadata
tags:
- summarization
- ar
- encoder-decoder
- xlm-roberta
- Abstractive Summarization
- roberta
- generated_from_trainer
datasets:
- xlsum
model-index:
- name: xlmroberta2xlmroberta-finetune-summarization-ar
results: []
xlmroberta2xlmroberta-finetune-summarization-ar
This model is a fine-tuned version of on the xlsum dataset. It achieves the following results on the evaluation set:
- Loss: 4.1298
- Rouge-1: 21.51
- Rouge-2: 8.14
- Rouge-l: 19.33
- Gen Len: 19.98
- Bertscore: 71.02
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
- 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
- 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 |
|---|---|---|---|---|---|---|---|---|
| 8.0645 | 1.0 | 1172 | 7.3567 | 8.22 | 0.66 | 7.94 | 20.0 | 58.18 |
| 7.2042 | 2.0 | 2344 | 6.6058 | 12.12 | 2.19 | 11.4 | 20.0 | 63.24 |
| 6.4168 | 3.0 | 3516 | 5.8784 | 16.46 | 4.31 | 15.15 | 20.0 | 66.3 |
| 5.4622 | 4.0 | 4688 | 4.7931 | 17.6 | 5.87 | 15.9 | 19.99 | 69.21 |
| 4.7829 | 5.0 | 5860 | 4.4418 | 19.17 | 6.74 | 17.22 | 19.98 | 70.23 |
| 4.4829 | 6.0 | 7032 | 4.2950 | 19.8 | 7.11 | 17.74 | 19.98 | 70.38 |
| 4.304 | 7.0 | 8204 | 4.2155 | 20.71 | 7.59 | 18.56 | 19.98 | 70.66 |
| 4.1778 | 8.0 | 9376 | 4.1632 | 21.1 | 7.94 | 18.99 | 19.98 | 70.86 |
| 4.0886 | 9.0 | 10548 | 4.1346 | 21.44 | 8.03 | 19.28 | 19.98 | 70.93 |
| 4.0294 | 10.0 | 11720 | 4.1298 | 21.51 | 8.14 | 19.33 | 19.98 | 71.02 |
Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1