metadata
license: apache-2.0
tags:
- summarization
- arabic
- ar
- en
- mt5
- Abstractive Summarization
- generated_from_trainer
datasets:
- xlsum
base_model: eslamxm/mt5-base-finetuned-english
model-index:
- name: mt5-base-finetuned-english-finetuned-english-arabic
results: []
mt5-base-finetuned-english-finetuned-english-arabic
This model is a fine-tuned version of 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