xlmroberta2xlmroberta-finetune-summarization-ar – AI Model by ahmeddbahaa | AlphaNeural AI

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ahmeddbahaa/
xlmroberta2xlmroberta-finetune-summarization-ar

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encoder-decoder

text2text-generation

Abstractive Summarization

generated_from_trainer

endpoints_compatible

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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: 21.69
Rouge-2: 8.73
Rouge-l: 19.52
Gen Len: 19.96
Bertscore: 71.0

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