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: 0.2966
Precision: 0.5681
Recall: 0.4022
F1: 0.4710
Accuracy: 0.9480

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	107	0.2496	0.5131	0.3624	0.4248	0.9450
No log	2.0	214	0.2794	0.5829	0.3485	0.4362	0.9456
No log	3.0	321	0.2808	0.5755	0.3781	0.4564	0.9465
No log	4.0	428	0.2935	0.5569	0.3902	0.4589	0.9476
0.059	5.0	535	0.2966	0.5681	0.4022	0.4710	0.9480

Framework versions

Transformers 4.41.2
Pytorch 2.0.1+cu117
Datasets 2.20.0
Tokenizers 0.19.1

stephen-osullivan/my_awesome_wnut_model

my_awesome_wnut_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

stephen-osullivan/
my_awesome_wnut_model