fine_tuned_mBERT_old – AI Model by morten-j | AlphaNeural AI

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morten-j/
fine_tuned_mBERT_old

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text-classification

generated_from_trainer

google-bert/bert-base-multilingual-cased

autotrain_compatible

endpoints_compatible

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fine_tuned_bert

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

Loss: 0.3681
F1: 0.7857
Precision: 0.8462
Recall: 0.7333
Accuracy: 0.8966

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: 5e-05
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
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Recall	Accuracy
No log	1.0	15	0.4987	0.6875	0.6471	0.7333	0.8276
No log	2.0	30	0.4779	0.625	0.5882	0.6667	0.7931
No log	3.0	45	0.4019	0.5833	0.7778	0.4667	0.8276
No log	4.0	60	0.6146	0.6522	0.4839	1.0	0.7241
No log	5.0	75	0.3311	0.7143	0.7692	0.6667	0.8621
No log	6.0	90	0.3877	0.7568	0.6364	0.9333	0.8448
No log	7.0	105	0.3971	0.7778	0.6667	0.9333	0.8621
No log	8.0	120	0.2041	0.8966	0.9286	0.8667	0.9483
No log	9.0	135	0.2831	0.875	0.8235	0.9333	0.9310
No log	10.0	150	0.2868	0.875	0.8235	0.9333	0.9310

Framework versions

Transformers 4.38.1
Pytorch 2.2.1+cu121
Datasets 2.17.0
Tokenizers 0.15.2