bart-noised-with-kaggle-dist – AI Model by gayanin | AlphaNeural AI

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gayanin/
bart-noised-with-kaggle-dist

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text2text-generation

generated_from_trainer

facebook/bart-base

endpoints_compatible

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bart-noised-with-kaggle-dist

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

Loss: 0.2336

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.4701	0.11	500	0.3771
0.3415	0.21	1000	0.3434
0.381	0.32	1500	0.3148
0.4196	0.43	2000	0.2986
0.3141	0.54	2500	0.3033
0.2984	0.64	3000	0.2834
0.2879	0.75	3500	0.2756
0.2906	0.86	4000	0.2646
0.346	0.96	4500	0.2594
0.2556	1.07	5000	0.2661
0.2264	1.18	5500	0.2611
0.2422	1.28	6000	0.2564
0.2103	1.39	6500	0.2543
0.2755	1.5	7000	0.2493
0.2587	1.61	7500	0.2473
0.2544	1.71	8000	0.2434
0.2136	1.82	8500	0.2383
0.2366	1.93	9000	0.2370
0.1862	2.03	9500	0.2455
0.1937	2.14	10000	0.2419
0.1673	2.25	10500	0.2405
0.1837	2.35	11000	0.2402
0.165	2.46	11500	0.2391
0.1725	2.57	12000	0.2352
0.1769	2.68	12500	0.2337
0.1347	2.78	13000	0.2355
0.1834	2.89	13500	0.2339
0.1505	3.0	14000	0.2336

Framework versions

Transformers 4.37.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.1