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:

• eval_loss: 0.2751
• eval_precision: 0.5915
• eval_recall: 0.2456
• eval_f1: 0.3471
• eval_accuracy: 0.9388
• eval_runtime: 3.0704
• eval_samples_per_second: 419.16
• eval_steps_per_second: 26.381
• epoch: 1.0
• step: 213

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
• distributed_type: multi-GPU
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Framework versions

• Transformers 4.36.1
• Pytorch 2.0.1+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0