checkpoints_2 – AI Model by BachNgoH | AlphaNeural AI

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BachNgoH/
checkpoints_2

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microsoft/deberta-v3-large

endpoints_compatible

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checkpoints_2

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: 0.8543
Map@3: 0.7167

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: 1
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Map@3
1.395	0.19	25	1.3859	0.5889
1.3803	0.37	50	1.3840	0.6958
1.3842	0.56	75	1.3314	0.7194
1.2795	0.74	100	1.0021	0.7222
0.9662	0.93	125	0.9006	0.6597
0.9574	1.11	150	0.8355	0.6903
0.8909	1.3	175	0.8506	0.6750
0.8077	1.48	200	0.8180	0.7125
0.955	1.67	225	0.8069	0.7097
0.8664	1.85	250	0.8186	0.7028
0.9396	2.04	275	0.8091	0.6986
0.8141	2.22	300	0.8212	0.7083
0.7898	2.41	325	0.8531	0.7167
0.9143	2.59	350	0.8482	0.7125
0.8861	2.78	375	0.8229	0.7083
0.8569	2.96	400	0.8372	0.7181
0.8381	3.15	425	0.8516	0.7153
0.7671	3.33	450	0.8458	0.7167
0.8704	3.52	475	0.8651	0.7222
0.8733	3.7	500	0.8356	0.7153
0.7309	3.89	525	0.8476	0.7181
0.7793	4.07	550	0.8566	0.7167
0.7849	4.26	575	0.8644	0.7167
0.7776	4.44	600	0.8584	0.7167
0.7573	4.63	625	0.8546	0.7167
0.8115	4.81	650	0.8543	0.7167
0.869	5.0	675	0.8543	0.7167

Framework versions

Transformers 4.35.0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.14.1