metacognitive-cls – AI Model by wesleymorris | AlphaNeural AI

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wesleymorris/
metacognitive-cls

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text-classification

generated_from_trainer

microsoft/deberta-v3-large

autotrain_compatible

endpoints_compatible

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metacognitive-cls

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: 0.1024
Accuracy: 0.9640
F1: 0.8326
Precision: 0.8742
Recall: 0.7947

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: 9.946303722432942e-06
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: linear
lr_scheduler_warmup_steps: 500
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.6685	1.0	76	0.6265	0.7931	0.0543	0.0559	0.0528
0.45	2.0	152	0.2973	0.8983	0.3275	0.6410	0.2199
0.2947	3.0	228	0.2671	0.9069	0.4910	0.6385	0.3988
0.2561	4.0	304	0.2246	0.9234	0.5323	0.8516	0.3871
0.2201	5.0	380	0.1926	0.9442	0.6988	0.8909	0.5748
0.1896	6.0	456	0.1704	0.9439	0.6828	0.9385	0.5367
0.1574	7.0	532	0.1468	0.9515	0.7452	0.9110	0.6305
0.1203	8.0	608	0.1213	0.9591	0.8056	0.8653	0.7537
0.0924	9.0	684	0.1119	0.9634	0.8290	0.8734	0.7889
0.0771	10.0	760	0.1073	0.9620	0.8206	0.8767	0.7713
0.067	11.0	836	0.1016	0.9657	0.8415	0.8762	0.8094
0.0609	12.0	912	0.1024	0.9640	0.8326	0.8742	0.7947

Framework versions

Transformers 4.40.1
Pytorch 2.3.0+cu121
Datasets 2.19.0
Tokenizers 0.19.1