finetuning-emotion-distilbert – AI Model by Suryakumar-P | AlphaNeural AI

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Suryakumar-P/
finetuning-emotion-distilbert

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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finetuning-emotion-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: 0.3198
Accuracy: 0.9405
F1: 0.9404

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	1.0	250	0.1581	0.9385	0.9390
0.1116	2.0	500	0.1468	0.9405	0.9408
0.1116	3.0	750	0.1533	0.9395	0.9399
0.076	4.0	1000	0.1673	0.9375	0.9373
0.076	5.0	1250	0.1808	0.9385	0.9387
0.0476	6.0	1500	0.2001	0.938	0.9381
0.0476	7.0	1750	0.2219	0.9375	0.9374
0.0302	8.0	2000	0.2741	0.9375	0.9375
0.0302	9.0	2250	0.2620	0.943	0.9431
0.0186	10.0	2500	0.2931	0.9405	0.9401
0.0186	11.0	2750	0.2850	0.934	0.9337
0.0153	12.0	3000	0.3024	0.9385	0.9384
0.0153	13.0	3250	0.2886	0.937	0.9370
0.0116	14.0	3500	0.2934	0.941	0.9414
0.0116	15.0	3750	0.3156	0.938	0.9379
0.0097	16.0	4000	0.3160	0.932	0.9318
0.0097	17.0	4250	0.3201	0.9375	0.9373
0.0077	18.0	4500	0.3139	0.9365	0.9366
0.0077	19.0	4750	0.3175	0.9405	0.9404
0.0061	20.0	5000	0.3198	0.9405	0.9404

Framework versions

Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.19.1