RuleBert-v0.1-k3 – AI Model by ribesstefano | AlphaNeural AI

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ribesstefano/
RuleBert-v0.1-k3

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text-classification

generated_from_trainer

classla/xlm-roberta-base-multilingual-text-genre-classifier

autotrain_compatible

endpoints_compatible

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ribesstefano/RuleBert-v0.1-k3

This model is a fine-tuned version of classla/xlm-roberta-base-multilingual-text-genre-classifier on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3285
F1: 0.4638
Roc Auc: 0.6576
Accuracy: 0.0714

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: 4
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.3423	0.13	250	0.3539	0.4497	0.6562	0.0670
0.3231	0.27	500	0.3425	0.4596	0.6594	0.0670
0.3248	0.4	750	0.3364	0.4495	0.6541	0.0714
0.3283	0.54	1000	0.3351	0.4529	0.6555	0.0714
0.3237	0.67	1250	0.3315	0.4600	0.6581	0.0625
0.325	0.81	1500	0.3313	0.4681	0.6624	0.0312
0.3316	0.94	1750	0.3290	0.4595	0.6564	0.0714
0.3239	1.08	2000	0.3310	0.4592	0.6572	0.0625
0.3085	1.21	2250	0.3280	0.4614	0.6567	0.0670
0.3161	1.35	2500	0.3303	0.4623	0.6574	0.0670
0.314	1.48	2750	0.3289	0.4613	0.6566	0.0714
0.3187	1.62	3000	0.3293	0.4594	0.6554	0.0714
0.3145	1.75	3250	0.3295	0.4629	0.6569	0.0714
0.3128	1.89	3500	0.3285	0.4629	0.6569	0.0714
0.3135	2.02	3750	0.3285	0.4615	0.6566	0.0714
0.3171	2.16	4000	0.3285	0.4638	0.6576	0.0714

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0