metadata
tags:
- summarization
- ar
- encoder-decoder
- mbert
- mbert2mbert
- Abstractive Summarization
- generated_from_trainer
datasets:
- wiki_lingua
model-index:
- name: mbert2mbert-finetuned-ar-wikilingua
results: []
mbert2mbert-finetuned-ar-wikilingua
This model is a fine-tuned version of on the wiki_lingua dataset. It achieves the following results on the evaluation set:
- Loss: 3.6753
- Rouge-1: 15.19
- Rouge-2: 5.45
- Rouge-l: 14.64
- Gen Len: 20.0
- Bertscore: 67.86
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: 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: 8
- label_smoothing_factor: 0.1
Training results
Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1