checkpoints_29_9_microsoft_deberta_V1 – AI Model by VuongQuoc | AlphaNeural AI

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checkpoints_29_9_microsoft_deberta_V1

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checkpoints_29_9_microsoft_deberta_V1

This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.7815
Map@3: 0.8290
Accuracy: 0.7333

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-06
train_batch_size: 1
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Map@3	Accuracy
1.6045	0.05	200	1.6095	0.4593	0.3030
1.3669	0.11	400	1.3360	0.7215	0.5980
0.9993	0.16	600	1.0403	0.7737	0.6727
0.9608	0.21	800	0.9539	0.7966	0.6990
0.9017	0.27	1000	0.9125	0.7997	0.6970
0.885	0.32	1200	0.8719	0.8172	0.7192
0.8222	0.37	1400	0.8462	0.8125	0.7030
0.769	0.43	1600	0.8376	0.8158	0.7131
0.7676	0.48	1800	0.8109	0.8178	0.7152
0.8413	0.53	2000	0.8279	0.8212	0.7212
0.809	0.59	2200	0.8012	0.8212	0.7212
0.8809	0.64	2400	0.8037	0.8290	0.7333
0.8028	0.69	2600	0.7949	0.8249	0.7293
0.8259	0.75	2800	0.7938	0.8283	0.7354
0.7548	0.8	3000	0.7818	0.8300	0.7354
0.7422	0.85	3200	0.7797	0.8316	0.7374
0.801	0.91	3400	0.7811	0.8303	0.7354
0.7	0.96	3600	0.7815	0.8290	0.7333

Framework versions

Transformers 4.32.1
Pytorch 2.0.0
Datasets 2.9.0
Tokenizers 0.13.3