distilbert-base-uncased-finetuned-sst2-midterm – AI Model by kbberendsen | AlphaNeural AI

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kbberendsen/
distilbert-base-uncased-finetuned-sst2-midterm

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-finetuned-sst2-midterm

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: 0.3341
Accuracy: 0.9048
F1: 0.9048
F1 0 Class: 0.9022
F1 1 Class: 0.9073
Precision 0 Class: 0.9097
Precision 1 Class: 0.9002
Recall 0 Class: 0.8949
Recall 1 Class: 0.9144

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: 64
eval_batch_size: 64
seed: 42
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	F1 0 Class	F1 1 Class	Precision 0 Class	Precision 1 Class	Recall 0 Class	Recall 1 Class
0.4244	1.0	109	0.2882	0.8842	0.8841	0.8790	0.8889	0.9017	0.8688	0.8575	0.9099
0.2289	2.0	218	0.2641	0.8922	0.8922	0.8889	0.8953	0.8995	0.8855	0.8785	0.9054
0.1517	3.0	327	0.3000	0.8933	0.8934	0.8930	0.8937	0.8798	0.9072	0.9065	0.8806
0.1048	4.0	436	0.3217	0.8991	0.8990	0.8952	0.9027	0.9126	0.8870	0.8785	0.9189
0.0767	5.0	545	0.3341	0.9048	0.9048	0.9022	0.9073	0.9097	0.9002	0.8949	0.9144

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2