OLID_OFFENSIVE_BERT_MULTILINGUAL – AI Model by christinacdl | AlphaNeural AI

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christinacdl/
OLID_OFFENSIVE_BERT_MULTILINGUAL

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text-classification

generated_from_trainer

christinacdl/OLID_Offensive

autotrain_compatible

endpoints_compatible

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OLID_OFFENSIVE_BERT_MULTILINGUAL

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

Loss: 0.6444
Macro F1: 0.7636
Micro F1: 0.7927
Accuracy: 0.7927

Performance on test set:

Accuracy: 0.9022540551927534
F1 score: 0.8855180494749837
Precision: 0.8690382339788112
Recall : 0.9122739652138543
Matthews Correlation Coefficient: 0.7801150070033589
Precision of each class: [0.97256778 0.76550868]
Recall of each class: [0.88969945 0.93484848]
F1 score of each class: [0.92928985 0.84174625]

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
lr_scheduler_warmup_steps: 500
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Macro F1	Micro F1	Accuracy
0.5568	1.0	744	0.4563	0.7641	0.7973	0.7973
0.4507	2.0	1488	0.4442	0.7657	0.8041	0.8041
0.3033	3.0	2232	0.5168	0.7672	0.7927	0.7927
0.2661	4.0	2976	0.6444	0.7636	0.7927	0.7927

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3