BERT_AA_IMDB_Top10_WithoutOOC_082023_MultilingualBertBase – AI Model by gregorgabrovsek | AlphaNeural AI

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BERT_AA_IMDB_Top10_WithoutOOC_082023_MultilingualBertBase

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text-classification

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autotrain_compatible

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BERT_AA_IMDB_Top10_WithoutOOC_082023_MultilingualBertBase

This model is a fine-tuned version of bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.5953
Accuracy: 0.8514
F1: 0.8339
Precision: 0.9032
Recall: 0.8514

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
0.1525	1.0	1041	1.0378	0.8286	0.8108	0.8693	0.8286
0.0992	2.0	2082	1.2450	0.8322	0.8087	0.8651	0.8322
0.0455	3.0	3123	1.3247	0.8425	0.8243	0.8908	0.8425
0.0257	4.0	4164	1.2742	0.8519	0.8302	0.8902	0.8519
0.0147	5.0	5205	1.3365	0.8505	0.8295	0.8907	0.8505
0.0049	6.0	6246	1.4477	0.8512	0.8326	0.8971	0.8512
0.0038	7.0	7287	1.5881	0.8445	0.8242	0.8938	0.8445
0.0043	8.0	8328	1.5755	0.85	0.8313	0.8951	0.85
0.0014	9.0	9369	1.5594	0.8512	0.8302	0.8948	0.8512
0.0011	10.0	10410	1.5953	0.8514	0.8339	0.9032	0.8514

Framework versions

Transformers 4.26.1
Pytorch 1.8.0
Datasets 2.10.1
Tokenizers 0.13.2