jim-crow-test – AI Model by evalstate | AlphaNeural AI

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evalstate/
jim-crow-test

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

text-embeddings-inference

endpoints_compatible

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jim-crow-test

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1308
Accuracy: 0.9748
F1: 0.9565
Precision: 0.9429
Recall: 0.9706

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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0611	1.0	90	0.1527	0.9552	0.9245	0.8909	0.9608
0.0686	2.0	180	0.1356	0.9636	0.9378	0.9159	0.9608
0.0052	3.0	270	0.1308	0.9748	0.9565	0.9429	0.9706
0.0206	4.0	360	0.1425	0.9636	0.9372	0.9238	0.9510
0.0049	5.0	450	0.1565	0.9692	0.9458	0.9505	0.9412

Framework versions

Transformers 5.7.0
Pytorch 2.11.0+cu130
Datasets 4.8.5
Tokenizers 0.22.2