BERT-multilingual-finetuned-CEFR_ner-3000news – AI Model by DioBot2000 | AlphaNeural AI

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DioBot2000/
BERT-multilingual-finetuned-CEFR_ner-3000news

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token-classification

generated_from_trainer

google-bert/bert-base-multilingual-cased

autotrain_compatible

endpoints_compatible

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BERT-multilingual-finetuned-CEFR_ner-3000news

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: 0.5691
Accuracy: 0.4044
Precision: 0.4949
Recall: 0.6593
F1: 0.4688

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	132	0.5657	0.3739	0.5044	0.5333	0.4050
No log	2.0	264	0.5076	0.3859	0.5011	0.5712	0.4225
No log	3.0	396	0.4845	0.3925	0.4690	0.6167	0.4351
0.4009	4.0	528	0.4981	0.3985	0.4956	0.6180	0.4514
0.4009	5.0	660	0.5136	0.3976	0.4913	0.6348	0.4570
0.4009	6.0	792	0.5092	0.4019	0.5004	0.6434	0.4655
0.4009	7.0	924	0.5235	0.4012	0.4837	0.6434	0.4555
0.1848	8.0	1056	0.5327	0.4033	0.4948	0.6519	0.4662
0.1848	9.0	1188	0.5640	0.4033	0.4920	0.6536	0.4638
0.1848	10.0	1320	0.5717	0.4031	0.4962	0.6547	0.4677
0.1848	11.0	1452	0.5667	0.4043	0.4910	0.6609	0.4666
0.1096	12.0	1584	0.5691	0.4044	0.4949	0.6593	0.4688

Framework versions

Transformers 4.41.1
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1