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

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lgk03/
WITHINAPPS_NDD-mantisbt_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-mantisbt_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: 0.0522
Accuracy: 0.9828
F1: 0.9833
Precision: 0.9847
Recall: 0.9828

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	71	0.0956	0.9762	0.9769	0.9787	0.9762
No log	2.0	142	0.0675	0.9770	0.9779	0.9804	0.9770
No log	3.0	213	0.0504	0.9823	0.9829	0.9847	0.9823
No log	4.0	284	0.0565	0.9828	0.9832	0.9844	0.9828
No log	5.0	355	0.0522	0.9828	0.9833	0.9847	0.9828

Framework versions

Transformers 4.42.4
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1