my_awesome_wnut_model

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

• Loss: 0.2845
• Precision: 0.5339
• Recall: 0.2697
• F1: 0.3584
• Accuracy: 0.9399

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.179	1.0	54	0.2887	0.5528	0.2475	0.3419	0.9388
0.1316	2.0	108	0.2845	0.5339	0.2697	0.3584	0.9399

Framework versions

• Transformers 4.57.2
• Pytorch 2.4.0+cu121
• Datasets 3.2.0
• Tokenizers 0.22.1