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Gliscor/
email-summarizer-bart-large-cnn-tr

Name: email-summarizer-bart-large-cnn-tr
Availability: InStock

Gliscor/email-summaries-tr

facebook/bart-large-cnn

mit

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email-summarization-mt5-lora

This model is a fine-tuned version of facebook/bart-large-cnn on Gliscor/email-summaries-tr dataset. It achieves the following results on the evaluation set:

Loss: 0.9852
Rouge1: 0.4139
Rouge2: 0.2289
Rougel: 0.3470
Meteor: 0.3850

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Meteor
1.3724	0.4444	1000	1.2582	0.3767	0.1882	0.3024	0.3463
1.2355	0.8889	2000	1.1349	0.3863	0.2006	0.3184	0.3568
1.091	1.3333	3000	1.0663	0.3848	0.2038	0.3182	0.3473
1.0423	1.7778	4000	1.0290	0.3989	0.2129	0.3332	0.3688
0.932	2.2222	5000	1.0098	0.3972	0.2167	0.3291	0.3611
1.0104	2.6667	6000	0.9852	0.4139	0.2289	0.3470	0.3850

Framework versions

PEFT 0.17.0
Transformers 4.55.1
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.4

Gliscor/email-summarizer-bart-large-cnn-tr

email-summarization-mt5-lora

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Gliscor/
email-summarizer-bart-large-cnn-tr