WITHINAPPS_NDD-dimeshift_test-content-CwAdj_EntireRepresentations – AI Model by lgk03 | AlphaNeural AI

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lgk03/
WITHINAPPS_NDD-dimeshift_test-content-CwAdj_EntireRepresentations

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distilbert/distilbert-base-uncased

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WITHINAPPS_NDD-dimeshift_test-content-CwAdj_EntireRepresentations

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

Loss: 0.5885
Accuracy: 0.5404
F1: 0.6364
Precision: 0.9173
Recall: 0.5404

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	0.9897	36	0.7413	0.0795	0.0117	0.0063	0.0795
No log	1.9794	72	0.6924	0.3865	0.4814	0.9056	0.3865
No log	2.9966	109	0.6657	0.4665	0.5674	0.9045	0.4665
No log	3.9863	145	0.6637	0.4282	0.5267	0.9099	0.4282
No log	4.9759	181	0.6404	0.4794	0.5790	0.9121	0.4794
No log	5.9931	218	0.6189	0.5469	0.6429	0.9118	0.5469
No log	6.9828	254	0.6017	0.5477	0.6433	0.9151	0.5477
No log	8.0	291	0.5944	0.5413	0.6373	0.9165	0.5413
No log	8.9897	327	0.5910	0.5537	0.6489	0.9138	0.5537
No log	9.8969	360	0.5885	0.5404	0.6364	0.9173	0.5404

Framework versions

Transformers 4.43.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1