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

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lgk03/
WITHINAPPS_NDD-phoenix_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-phoenix_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: 0.0023
Accuracy: 0.9978
F1: 0.9978
Precision: 0.9978
Recall: 0.9978

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	1.0	70	0.0076	0.9964	0.9964	0.9964	0.9964
No log	2.0	140	0.0054	0.9964	0.9964	0.9964	0.9964
No log	3.0	210	0.0042	0.9969	0.9969	0.9969	0.9969
No log	4.0	280	0.0027	0.9978	0.9978	0.9978	0.9978
No log	5.0	350	0.0023	0.9978	0.9978	0.9978	0.9978

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1