NASca-finetuned-diego-2 – AI Model by dtorber | AlphaNeural AI

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NASca-finetuned-diego-2

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NASca-finetuned-diego-2

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

Loss: 1.9197
Rouge1: 0.2930
Rouge2: 0.1226
Rougel: 0.2327
Rougelsum: 0.2373

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: 1.3739167643078955e-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
1.4353	0.09	15000	1.7848	0.2816	0.1157	0.2258	0.2305
1.3237	1.0	159149	1.7707	0.2831	0.1171	0.2267	0.2318
1.3836	2.0	318298	1.7924	0.2899	0.1211	0.2316	0.2361
1.274	3.0	477447	1.8809	0.2921	0.1225	0.2323	0.2368
1.2289	4.0	636596	1.9128	0.2926	0.1223	0.2322	0.2368
1.2424	5.0	795745	1.9197	0.2930	0.1226	0.2327	0.2373

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu117
Datasets 2.9.0
Tokenizers 0.13.2