emotion-classification-distilbert – AI Model by Shihao-Deng | AlphaNeural AI

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Shihao-Deng/
emotion-classification-distilbert

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

endpoints_compatible

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emotion-classification-distilbert

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: 0.0627
Accuracy: 0.9661
Precision: 0.9670
Recall: 0.9661
F1: 0.9664

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: 512
eval_batch_size: 512
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 2048
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6677	1.0	135	0.1096	0.9610	0.9618	0.9610	0.9613
0.0901	2.0	270	0.0695	0.9664	0.9673	0.9664	0.9666
0.0713	3.0	405	0.0635	0.9668	0.9682	0.9668	0.9671
0.0681	4.0	540	0.0618	0.9670	0.9679	0.9670	0.9673

Framework versions

Transformers 4.57.3
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1