XLM_RoBERTa-Multilingual-OpusMT-Offensive-Language-Detection

This model is a fine-tuned version of xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.7772
• Micro F1: 0.8762
• Macro F1: 0.8659
• Accuracy: 0.8762

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Framework versions

• Transformers 4.36.1
• Pytorch 2.1.0+cu121
• Datasets 2.13.1
• Tokenizers 0.15.0