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AIKIA_0.6_OFFENSIVE_BERT_MULTILINGUAL
This model is a fine-tuned version of bert-base-multilingual-uncased on the AIKIA Greek dataset with 0.6 threshold for the Offensive label. It achieves the following results on the evaluation set:
- Loss: 1.5183
- Macro F1: 0.6983
- Micro F1: 0.7565
- Accuracy: 0.7565
Results on test set:
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Accuracy: 0.7784313725490196
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F1 score: 0.7575514222182127
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Precision: 0.7545776520467404
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Recall : 0.7612935323383084
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Matthews Correlation Coefficient: 0.5158274671154518
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Precision of each class: [0.84188912 0.66726619]
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Recall of each class: [0.8159204 0.70666667]
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F1 score of each class: [0.82870136 0.68640148]
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Micro F1 | Accuracy |
|---|---|---|---|---|---|---|
| No log | 1.0 | 338 | 0.5701 | 0.7024 | 0.7122 | 0.7122 |
| 0.5808 | 2.0 | 676 | 0.5355 | 0.7338 | 0.7565 | 0.7565 |
| 0.4115 | 3.0 | 1014 | 0.6522 | 0.7206 | 0.7528 | 0.7528 |
| 0.4115 | 4.0 | 1352 | 0.8994 | 0.6287 | 0.7306 | 0.7306 |
| 0.2334 | 5.0 | 1690 | 0.9278 | 0.7000 | 0.7417 | 0.7417 |
| 0.1489 | 6.0 | 2028 | 1.3355 | 0.7048 | 0.7491 | 0.7491 |
| 0.1489 | 7.0 | 2366 | 1.5183 | 0.6983 | 0.7565 | 0.7565 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
