SloBertAA_Top20_WithoutOOC_082023_MultilingualBertBase – AI Model by gregorgabrovsek | AlphaNeural AI

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SloBertAA_Top20_WithoutOOC_082023_MultilingualBertBase

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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SloBertAA_Top20_WithoutOOC_082023_MultilingualBertBase

This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.2075
Accuracy: 0.8470
F1: 0.8471
Precision: 0.8475
Recall: 0.8470

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: 12
eval_batch_size: 12
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.6541	1.0	22717	0.6481	0.7923	0.7920	0.8008	0.7923
0.5254	2.0	45434	0.5756	0.8198	0.8204	0.8268	0.8198
0.3977	3.0	68151	0.5874	0.8279	0.8285	0.8331	0.8279
0.3059	4.0	90868	0.6427	0.8357	0.8357	0.8378	0.8357
0.2596	5.0	113585	0.7435	0.8378	0.8377	0.8403	0.8378
0.1706	6.0	136302	0.8755	0.8413	0.8407	0.8415	0.8413
0.146	7.0	159019	1.0080	0.8397	0.8393	0.8405	0.8397
0.0945	8.0	181736	1.1210	0.8422	0.8423	0.8441	0.8422
0.0534	9.0	204453	1.1898	0.8448	0.8449	0.8457	0.8448
0.0286	10.0	227170	1.2075	0.8470	0.8471	0.8475	0.8470

Framework versions

Transformers 4.26.1
Pytorch 1.8.0
Datasets 2.10.1
Tokenizers 0.13.2