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notBanana/
SummarEaseElementaryV4

Name: SummarEaseElementaryV4
Availability: InStock

generated_from_trainer

notBanana/SummarEaseElementaryV3

finetune

endpoints_compatible

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SummarEaseElementaryV4

This model is a fine-tuned version of notBanana/SummarEaseElementaryV3 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.2757
Rouge1: 0.1334
Rouge2: 0.0545
Rougel: 0.115
Rougelsum: 0.1147
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: 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
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	1	2.2787	0.1259	0.0521	0.1093	0.1098	20.0
No log	2.0	2	2.2686	0.125	0.0482	0.1055	0.106	20.0
No log	3.0	3	2.2718	0.1281	0.0482	0.1083	0.1086	20.0
No log	4.0	4	2.2757	0.1334	0.0545	0.115	0.1147	20.0

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

notBanana/SummarEaseElementaryV4

SummarEaseElementaryV4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

notBanana/
SummarEaseElementaryV4