DistilBERT – AI Model by zonghaoyang | AlphaNeural AI

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zonghaoyang/
DistilBERT

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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DistilBERT

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: 0.2949
Accuracy: {'accuracy': 0.9019555317439057}
F1 Score: {'f1': 0.6131078224101479}
Precision: {'precision': 0.6697459584295612}
Recall: {'recall': 0.5653021442495126}

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Precision	Recall
0.2408	1.0	1626	0.2795	{'accuracy': 0.9049022234128047}	{'f1': 0.5872093023255814}	{'precision': 0.7276657060518732}	{'recall': 0.4922027290448343}
0.1928	2.0	3252	0.2949	{'accuracy': 0.9019555317439057}	{'f1': 0.6131078224101479}	{'precision': 0.6697459584295612}	{'recall': 0.5653021442495126}

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3