distilbert_token_classification_finetuned

This model is a fine-tuned version of distilbert/distilbert-base-uncased on the flaitenberger/wnut_17 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2421
• Precision: 0.6564
• Recall: 0.4593
• F1: 0.5405
• Accuracy: 0.9507

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: 32
• eval_batch_size: 128
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 5.22.1
• Pytorch 2.14.1+cu130
• Datasets 4.8.5
• Tokenizers 0.23.3