results – AI Model by lrycro | AlphaNeural AI

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text-classification

generated_from_trainer

google-bert/bert-base-multilingual-cased

autotrain_compatible

endpoints_compatible

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results

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

Loss: 0.2090
Accuracy: 0.9467
F1: 0.9463
Precision: 0.9469
Recall: 0.9467

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: 8
eval_batch_size: 8
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
0.4561	1.0	1098	0.3688	0.8738	0.8709	0.8760	0.8738
0.3126	2.0	2196	0.2254	0.9339	0.9335	0.9340	0.9339
0.3852	3.0	3294	0.3113	0.9280	0.9274	0.9284	0.9280
0.2341	4.0	4392	0.2376	0.9417	0.9417	0.9418	0.9417
0.2108	5.0	5490	0.2433	0.9408	0.9407	0.9407	0.9408
0.0882	6.0	6588	0.2353	0.9371	0.9364	0.9384	0.9371
0.127	7.0	7686	0.2674	0.9276	0.9270	0.9277	0.9276
0.1413	8.0	8784	0.2859	0.9339	0.9341	0.9344	0.9339
0.7061	9.0	9882	0.6121	0.6761	0.5834	0.7861	0.6761
0.2076	10.0	10980	0.2090	0.9467	0.9463	0.9469	0.9467

Framework versions

Transformers 4.39.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2