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

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lgk03/
WITHINAPPS_NDD-dimeshift_test-content-CWAdj

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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WITHINAPPS_NDD-dimeshift_test-content-CWAdj

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: 0.1842
Accuracy: 0.8908
F1: 0.9094
Precision: 0.9524
Recall: 0.8908

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: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	0.9897	72	0.2950	0.8323	0.8667	0.9438	0.8323
No log	1.9931	145	0.2058	0.8852	0.9053	0.9520	0.8852
No log	2.9966	218	0.1973	0.8852	0.9054	0.9525	0.8852
No log	4.0	291	0.1926	0.8869	0.9065	0.9517	0.8869
No log	4.9485	360	0.1842	0.8908	0.9094	0.9524	0.8908

Framework versions

Transformers 4.42.4
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1