token_classification_model

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

• Loss: 0.2939
• Precision: 0.4838
• Recall: 0.2215
• F1: 0.3039
• Accuracy: 0.9368

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: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Framework versions

• Transformers 4.28.0
• Pytorch 1.12.1
• Datasets 2.14.4
• Tokenizers 0.13.3