This model is a fine-tuned version of microsoft/deberta-v3-large on the imdb dataset. It achieves the following results on the evaluation set @ epoch 9 of 10, which is loaded as the best model here:

Accuracy: 0.9656
F1: 0.9657
Precision: 0.9640
Recall: 0.9673

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: 2e-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: cosine
lr_scheduler_warmup_ratio: 0.2
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.2279	1.0	3125	0.1466	0.9603	0.9599	0.9693	0.9506
0.2689	2.0	6250	0.1929	0.9550	0.9546	0.9626	0.9467
0.1728	3.0	9375	0.1807	0.9584	0.9579	0.9697	0.9463
0.1937	4.0	12500	0.1734	0.9435	0.9457	0.9102	0.9841
0.2044	5.0	15625	0.2102	0.9510	0.9523	0.9272	0.9788
0.0484	6.0	18750	0.2134	0.9593	0.9599	0.9448	0.9756
0.0336	7.0	21875	0.2278	0.9610	0.9614	0.9524	0.9706
0.0704	8.0	25000	0.2039	0.9648	0.9651	0.9581	0.9721
0.0004	9.0	28125	0.2241	0.9656	0.9657	0.9640	0.9673
0.0004	10.0	31250	0.2233	0.9653	0.9654	0.9637	0.9670

Framework versions

Transformers 4.39.2
Pytorch 2.2.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2

dfurman/deberta-v3-large-imdb-v0.2

deberta-v3-large-imdb-v0.2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dfurman/
deberta-v3-large-imdb-v0.2