summarise_v2 – AI Model by debbiesoon | AlphaNeural AI

You can deploy this model and start earning money today!

debbiesoon/
summarise_v2

like

0

text2text-generation

generated_from_trainer

endpoints_compatible

Views

No views yet

summarise_v2

This model is a fine-tuned version of allenai/led-base-16384 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.3235
Rouge2 Precision: 0.018
Rouge2 Recall: 0.0916
Rouge2 Fmeasure: 0.0292

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: 2
eval_batch_size: 2
seed: 42
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
3.1721	0.08	10	2.7742	0.0107	0.0671	0.0178
3.0802	0.16	20	2.7914	0.0111	0.0878	0.019
3.0795	0.24	30	2.6954	0.0094	0.076	0.0157
2.5806	0.32	40	2.6587	0.0028	0.0271	0.0046
2.6553	0.4	50	2.5958	0.0084	0.0566	0.0143
2.689	0.48	60	2.4857	0.0089	0.0733	0.015
2.6642	0.56	70	2.4205	0.0069	0.0478	0.0116
2.3768	0.64	80	2.3754	0.0127	0.0795	0.0215
2.1949	0.72	90	2.3752	0.0155	0.1013	0.0258
2.3257	0.8	100	2.3509	0.0155	0.1011	0.0261
2.4053	0.88	110	2.3261	0.015	0.0901	0.0246
2.9896	0.96	120	2.3235	0.018	0.0916	0.0292

Framework versions

Transformers 4.21.3
Pytorch 1.12.1+cu113
Datasets 1.2.1
Tokenizers 0.12.1