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: 0.3410
Precision: 0.5736
Recall: 0.3577
F1: 0.4406
Accuracy: 0.9460

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	107	0.2753	0.6224	0.2734	0.3799	0.9402
No log	2.0	214	0.2722	0.5637	0.3197	0.4080	0.9447
No log	3.0	321	0.3145	0.5789	0.3401	0.4285	0.9458
No log	4.0	428	0.3199	0.5218	0.3550	0.4225	0.9449
0.0914	5.0	535	0.3410	0.5736	0.3577	0.4406	0.9460

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 3.6.0
Tokenizers 0.22.0

bastpy/the_first_model_develop_wnut

the_first_model_develop_wnut

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bastpy/
the_first_model_develop_wnut