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alphahg/
kobart-base-v2-finetuned-paper

Name: kobart-base-v2-finetuned-paper
Availability: InStock

generated_from_trainer

aihub_paper_summarization

mit

model-index

endpoints_compatible

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kobart-base-v2-finetuned-paper

This model is a fine-tuned version of gogamza/kobart-base-v2 on the aihub_paper_summarization dataset. It achieves the following results on the evaluation set:

Loss: 1.2966
Rouge1: 6.2883
Rouge2: 1.7038
Rougel: 6.2556
Rougelsum: 6.2618
Gen Len: 20.0

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: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.2215	1.0	8831	1.3293	6.2425	1.7317	6.2246	6.2247	20.0
1.122	2.0	17662	1.3056	6.2298	1.7005	6.2042	6.2109	20.0
1.0914	3.0	26493	1.2966	6.2883	1.7038	6.2556	6.2618	20.0

Framework versions

Transformers 4.25.1
Pytorch 1.13.1
Datasets 2.8.0
Tokenizers 0.13.2

alphahg/kobart-base-v2-finetuned-paper

kobart-base-v2-finetuned-paper

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

alphahg/
kobart-base-v2-finetuned-paper