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: 0.5696
Recall: 0.2956
F1: 0.3893
Accuracy: 0.9416

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	0.2813	0.5089	0.2382	0.3245	0.9381
No log	2.0	426	0.2734	0.5696	0.2956	0.3893	0.9416

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu118
Datasets 2.19.0
Tokenizers 0.19.1

PradhyumnaPoralla/pp_wnut_model

pp_wnut_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

PradhyumnaPoralla/
pp_wnut_model