bert-base-multilingual-uncased-finetuned-ner-generated – AI Model by noor-raghib-12 | AlphaNeural AI | AlphaNeural AI

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noor-raghib-12/
bert-base-multilingual-uncased-finetuned-ner-generated

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token-classification

generated_from_trainer

google-bert/bert-base-multilingual-uncased

autotrain_compatible

endpoints_compatible

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bert-base-multilingual-uncased-finetuned-ner-generated

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

Loss: 0.6112
Precision: 0.6290
Recall: 0.5107
F1: 0.5637
Accuracy: 0.8500

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: 50
eval_batch_size: 50
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	353	0.7936	0.6247	0.3256	0.4281	0.8176
0.7425	2.0	706	0.6785	0.6438	0.4272	0.5136	0.8385
0.5796	3.0	1059	0.6290	0.6251	0.4863	0.5471	0.8450
0.5796	4.0	1412	0.6145	0.6367	0.5027	0.5618	0.8503
0.4924	5.0	1765	0.6112	0.6290	0.5107	0.5637	0.8500

Framework versions

Transformers 4.54.0
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.21.2