reward-deberta-v3 – AI Model by Chat-Error | AlphaNeural AI

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Chat-Error/
reward-deberta-v3

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text-classification

generated_from_trainer

microsoft/deberta-v3-base

autotrain_compatible

endpoints_compatible

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This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6774
F1: 0.7444

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: 6e-06
train_batch_size: 3
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 48
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 5.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
0.6448	0.21	500	0.6347	0.6498
0.6401	0.41	1000	0.6442	0.6312
0.6557	0.62	1500	0.6582	0.6314
0.5819	0.83	2000	0.6588	0.6320
0.6086	1.04	2500	0.6563	0.6343
0.6011	1.24	3000	0.6557	0.6165
0.5616	1.45	3500	0.6461	0.6376
0.5885	1.66	4000	0.6468	0.6304
0.6198	1.87	4500	0.6423	0.6448
0.5838	2.07	5000	0.6665	0.6320
0.5564	2.28	5500	0.6684	0.6428
0.5726	2.49	6000	0.6703	0.6401
0.5491	2.7	6500	0.6684	0.6455
0.5303	2.9	7000	0.6703	0.6339
0.497	3.11	7500	0.6607	0.6541
0.5041	3.32	8000	0.6760	0.6653
0.4978	3.53	8500	0.6696	0.6627
0.5272	3.73	9000	0.6677	0.6684
0.5487	3.94	9500	0.6760	0.6593
0.4998	4.15	10000	0.6747	0.6738
0.4626	4.36	10500	0.6753	0.6781
0.5202	4.56	11000	0.6722	0.6763
0.4623	4.77	11500	0.6728	0.6778
0.4383	4.98	12000	0.6741	0.6775

Framework versions

Transformers 4.35.0.dev0
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.14.1