FLANG-ELECTRA_flang-bert – AI Model by avinasht | AlphaNeural AI

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avinasht/
FLANG-ELECTRA_flang-bert

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text-classification

generated_from_trainer

SALT-NLP/FLANG-ELECTRA

autotrain_compatible

endpoints_compatible

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FLANG-ELECTRA_flang-bert

This model is a fine-tuned version of SALT-NLP/FLANG-ELECTRA on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5930
Accuracy: 0.8705
F1: 0.8717
Precision: 0.8772
Recall: 0.8705

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.6377	1.0	181	0.5174	0.8003	0.7860	0.8080	0.8003
0.4035	2.0	362	0.4221	0.8580	0.8578	0.8611	0.8580
0.2395	3.0	543	0.4535	0.8580	0.8560	0.8592	0.8580
0.231	4.0	724	0.4335	0.8658	0.8657	0.8659	0.8658
0.3369	5.0	905	0.5608	0.8081	0.8057	0.8151	0.8081
0.2203	6.0	1086	0.5002	0.8705	0.8691	0.8706	0.8705
0.239	7.0	1267	0.6676	0.8128	0.8125	0.8338	0.8128
0.0938	8.0	1448	0.5930	0.8705	0.8717	0.8772	0.8705
0.1329	9.0	1629	0.5017	0.8580	0.8571	0.8572	0.8580
0.3598	10.0	1810	0.5126	0.8690	0.8675	0.8698	0.8690
0.1615	11.0	1991	0.5945	0.8612	0.8605	0.8606	0.8612
0.0923	12.0	2172	0.8213	0.8268	0.8292	0.8450	0.8268
0.1296	13.0	2353	0.8647	0.8580	0.8586	0.8611	0.8580

Framework versions

Transformers 4.37.0
Pytorch 2.1.2
Datasets 2.1.0
Tokenizers 0.15.1