finetuning-sentiment – AI Model by harshavsk | AlphaNeural AI

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harshavsk/
finetuning-sentiment

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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finetuning-sentiment

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: 0.8125
Accuracy@en: 0.9033
F1@en: 0.9002
Precision@en: 0.8989
Recall@en: 0.9018
Loss@en: 0.8125

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: 8
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 13

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy@en	F1@en	Precision@en	Recall@en	Loss@en
No log	1.0	375	0.4653	0.8933	0.8895	0.8895	0.8895	0.4653
0.2086	2.0	750	0.4367	0.9033	0.9011	0.8979	0.9069	0.4367
0.1622	3.0	1125	0.4866	0.91	0.9081	0.9047	0.9151	0.4866
0.0622	4.0	1500	0.6156	0.9	0.8982	0.8951	0.9067	0.6156
0.0622	5.0	1875	0.6790	0.9133	0.9102	0.9102	0.9102	0.6790
0.0193	6.0	2250	0.6822	0.9	0.8978	0.8945	0.9041	0.6822
0.0202	7.0	2625	0.6595	0.91	0.9077	0.9047	0.9126	0.6595
0.0148	8.0	3000	0.6538	0.9067	0.9042	0.9014	0.9085	0.6538
0.0148	9.0	3375	0.6869	0.9067	0.9050	0.9018	0.9136	0.6869
0.0036	10.0	3750	0.7016	0.9033	0.9007	0.8981	0.9044	0.7016
0.0038	11.0	4125	0.8170	0.9	0.8961	0.8972	0.8951	0.8170
0.008	12.0	4500	0.8169	0.9033	0.9002	0.8989	0.9018	0.8169
0.008	13.0	4875	0.8125	0.9033	0.9002	0.8989	0.9018	0.8125

Framework versions

Transformers 4.17.0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2