xlnet-large-cased-finetuned-augmentation-LUNAR-TAPT – AI Model by sercetexam9 | AlphaNeural AI

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xlnet-large-cased-finetuned-augmentation-LUNAR-TAPT

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text-classification

generated_from_trainer

xlnet/xlnet-large-cased

autotrain_compatible

endpoints_compatible

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xlnet-large-cased-finetuned-augmentation-LUNAR-TAPT

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

Loss: 0.4904
F1: 0.8291
Roc Auc: 0.8646
Accuracy: 0.6215

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_steps: 100
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.3732	1.0	318	0.3677	0.6417	0.7249	0.4227
0.291	2.0	636	0.2986	0.7666	0.8238	0.5426
0.2296	3.0	954	0.2937	0.7774	0.8291	0.5552
0.1332	4.0	1272	0.3269	0.7980	0.8559	0.5797
0.0964	5.0	1590	0.3768	0.7977	0.8473	0.5505
0.0618	6.0	1908	0.4196	0.7833	0.8416	0.5552
0.0356	7.0	2226	0.4305	0.8041	0.8509	0.5726
0.0214	8.0	2544	0.4510	0.8112	0.8482	0.5883
0.0196	9.0	2862	0.4708	0.8118	0.8582	0.5970
0.0111	10.0	3180	0.4950	0.8174	0.8590	0.5994
0.0124	11.0	3498	0.5083	0.8094	0.8572	0.5852
0.0079	12.0	3816	0.4904	0.8291	0.8646	0.6215
0.0062	13.0	4134	0.5218	0.8155	0.8578	0.5954
0.001	14.0	4452	0.5225	0.8194	0.8636	0.6073
0.0024	15.0	4770	0.5248	0.8244	0.8646	0.6088
0.0012	16.0	5088	0.5259	0.8235	0.8652	0.6073

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0