tokenclass-wnut
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: 0.2858
Precision: 0.4846
Recall: 0.2632
F1: 0.3411
Accuracy: 0.9386
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
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2
Training results
Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 213 0.2976 0.3873 0.1974 0.2615 0.9352
No log 2.0 426 0.2858 0.4846 0.2632 0.3411 0.9386
Framework versions
Transformers 4.20.1
Pytorch 1.11.0+cpu
Datasets 2.1.0
Tokenizers 0.12.1
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