This model fine-tunes bert-base-uncased on the CoNLL-2003 dataset for part-of-speech (POS) tagging. It is trained to label each token in a sentence with its corresponding POS tag, achieving high precision and recall on the evaluation set. Suitable for NLP tasks that require accurate grammatical structure identification in English text.

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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2395	1.0	1756	0.2447	0.9170	0.9157	0.9164	0.9417
0.1589	2.0	3512	0.2177	0.9245	0.9209	0.9227	0.9463
0.1191	3.0	5268	0.2140	0.9258	0.9257	0.9257	0.9483

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

yashparalkar0/bert-finetuned-pos

bert-finetuned-pos

Model description