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: 0.3052
Precision: 0.5218
Recall: 0.3874
F1: 0.4447
Accuracy: 0.9463

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2801	0.5586	0.2428	0.3385	0.9384
No log	2.0	426	0.2573	0.5228	0.2975	0.3792	0.9425
0.1769	3.0	639	0.2859	0.5510	0.3253	0.4091	0.9450
0.1769	4.0	852	0.2965	0.5499	0.3522	0.4294	0.9462
0.0496	5.0	1065	0.2951	0.5123	0.3846	0.4394	0.9458
0.0496	6.0	1278	0.3052	0.5218	0.3874	0.4447	0.9463

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

BaselMousi/distilbert_wnut_model

distilbert_wnut_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

BaselMousi/
distilbert_wnut_model