metadata

tags:
  - summarization
  - mT5_multilingual_XLSum
  - mt5
  - abstractive summarization
  - ar
  - xlsum
  - generated_from_trainer
datasets:
  - xlsum
base_model: csebuetnlp/mT5_multilingual_XLSum
model-index:
  - name: mT5_multilingual_XLSum-finetune-ar-xlsum
    results: []

mT5_multilingual_XLSum-finetune-ar-xlsum

This model is a fine-tuned version of csebuetnlp/mT5_multilingual_XLSum on the xlsum dataset. It achieves the following results on the evaluation set:

Loss: 3.2497
Rouge-1: 32.52
Rouge-2: 14.71
Rouge-l: 27.88
Gen Len: 41.45
Bertscore: 74.65

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: 16
total_train_batch_size: 64
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

Training Loss	Epoch	Step	Validation Loss	Rouge-1	Rouge-2	Rouge-l	Gen Len	Bertscore
3.5465	1.0	585	3.3215	30.09	13.23	26.07	36.31	73.97
3.3564	2.0	1170	3.2547	31.29	13.93	26.75	41.68	74.22
3.2185	3.0	1755	3.2421	31.78	14.1	27.07	41.64	74.4
3.1145	4.0	2340	3.2241	31.98	14.38	27.51	40.29	74.46
3.031	5.0	2925	3.2313	32.3	14.67	27.83	39.81	74.61
2.9627	6.0	3510	3.2348	32.39	14.65	27.76	40.02	74.6
2.9088	7.0	4095	3.2439	32.5	14.66	27.81	41.2	74.65
2.8649	8.0	4680	3.2497	32.52	14.71	27.88	41.45	74.65

Framework versions

Transformers 4.19.4
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1