token_classification_DistilBERT_WNUT17

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: 0.2914
Precision: 0.6667
Recall: 0.5311
F1: 0.5912
Accuracy: 0.9543

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0319	1.0	213	0.2548	0.6438	0.5383	0.5863	0.9536
0.0235	2.0	426	0.2695	0.6528	0.5443	0.5936	0.9542
0.0148	3.0	639	0.2946	0.7003	0.5144	0.5931	0.9544
0.0131	4.0	852	0.2931	0.6785	0.5251	0.5920	0.9549
0.0134	5.0	1065	0.2914	0.6667	0.5311	0.5912	0.9543

Framework versions

Transformers 4.51.3
Pytorch 2.7.0
Datasets 3.6.0
Tokenizers 0.21.2

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Swagam/token_classification_DistilBERT_WNUT17

token_classification_DistilBERT_WNUT17

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

token_classification_DistilBERT_WNUT17

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions