summarise_v5 – AI Model by debbiesoon | AlphaNeural AI

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summarise_v5

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text2text-generation

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summarise_v5

This model is a fine-tuned version of allenai/led-base-16384 on the multi_news dataset. It achieves the following results on the evaluation set:

Loss: 2.3252
Rouge2 Precision: 0.1458
Rouge2 Recall: 0.1306
Rouge2 Fmeasure: 0.1343

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: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge2 Precision	Rouge2 Recall	Rouge2 Fmeasure
2.6266	0.13	10	2.4604	0.1021	0.179	0.124
2.4818	0.27	20	2.4122	0.1402	0.1422	0.1345
2.3451	0.4	30	2.3846	0.1631	0.1177	0.1307
2.4462	0.53	40	2.3584	0.1671	0.1175	0.133
2.443	0.67	50	2.3395	0.1444	0.1359	0.1344
2.3822	0.8	60	2.3377	0.1517	0.1411	0.1395
2.4304	0.93	70	2.3252	0.1458	0.1306	0.1343

Framework versions

Transformers 4.21.3
Pytorch 1.12.1+cu113
Datasets 2.6.2.dev0
Tokenizers 0.12.1