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: 0.2734
Precision: 0.5518
Recall: 0.3457
F1: 0.4251
Accuracy: 0.9437

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	425	0.2845	0.6045	0.3216	0.4198	0.9421
0.2229	2.0	850	0.2734	0.5518	0.3457	0.4251	0.9437

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 3.6.0
Tokenizers 0.22.2

MohamedAliAhmed/first_token_classification_wnut_distilbert_model

first_token_classification_wnut_distilbert_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MohamedAliAhmed/
first_token_classification_wnut_distilbert_model