bert_12_layer_model_v1_complete_training_new_emb_compress_48_gelu – AI Model by gokuls | AlphaNeural AI

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bert_12_layer_model_v1_complete_training_new_emb_compress_48_gelu

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bert_12_layer_model_v1_complete_training_new_emb_compress_48_gelu

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

Loss: 4.7019
Accuracy: 0.3153

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: 1e-05
train_batch_size: 48
eval_batch_size: 48
seed: 10
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10000
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
7.1202	0.08	10000	7.0974	0.0833
6.6953	0.16	20000	6.6906	0.1078
6.549	0.25	30000	6.5434	0.1182
6.463	0.33	40000	6.4530	0.1247
6.39	0.41	50000	6.3876	0.1310
6.3422	0.49	60000	6.3315	0.1343
6.2978	0.57	70000	6.2912	0.1374
6.2666	0.66	80000	6.2620	0.1398
6.2338	0.74	90000	6.2298	0.1421
6.2068	0.82	100000	6.2109	0.1437
6.1882	0.9	110000	6.1852	0.1441
6.1676	0.98	120000	6.1647	0.1457
6.1309	1.07	130000	6.1335	0.1469
6.0822	1.15	140000	6.0727	0.1483
5.9914	1.23	150000	5.9807	0.1540
5.8946	1.31	160000	5.8634	0.1771
5.6741	1.39	170000	5.6228	0.2136
5.3973	1.47	180000	5.3334	0.2463
5.0432	1.56	190000	4.9868	0.2842
4.7647	1.64	200000	4.7019	0.3153

Framework versions

Transformers 4.30.2
Pytorch 1.14.0a0+410ce96
Datasets 2.13.1
Tokenizers 0.13.3