---
metrics:
- accuracy
- precision
pipeline_tag: token-classification
---
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