distilbert_base_uncased_SST2_finetune – AI Model by songyi-ng | AlphaNeural AI

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distilbert_base_uncased_SST2_finetune

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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distilbert_base_uncased_SST2_finetune

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

Loss: 0.3630
Accuracy: 0.8372
F1: 0.8371
Precision: 0.8373
Recall: 0.8372
Learning Rate: 0.0000

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Rate
0.4616	1.0	8419	0.3845	0.8337	0.8334	0.8350	0.8337	0.0000
0.3644	2.0	16838	0.3730	0.8291	0.8291	0.8300	0.8291	0.0000
0.3526	3.0	25257	0.3661	0.8280	0.8277	0.8290	0.8280	0.0000
0.346	4.0	33676	0.3709	0.8349	0.8345	0.8369	0.8349	0.0000
0.3436	5.0	42095	0.3674	0.8383	0.8383	0.8384	0.8383	0.0000
0.3412	6.0	50514	0.3630	0.8372	0.8371	0.8373	0.8372	0.0000

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3