NER_Pittsburgh_TAA – AI Model by TymofiiT | AlphaNeural AI

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TymofiiT/
NER_Pittsburgh_TAA

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token-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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NER_Pittsburgh_TAA

This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:

Loss: 0.0599
Precision: 0.9344
Recall: 0.9461
F1: 0.9402
Accuracy: 0.9858

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	439	0.0604	0.9175	0.9290	0.9232	0.9829
0.0953	2.0	878	0.0545	0.9312	0.9412	0.9361	0.9850
0.0409	3.0	1317	0.0571	0.9357	0.9412	0.9384	0.9855
0.0234	4.0	1756	0.0593	0.9343	0.9482	0.9412	0.9858
0.0159	5.0	2195	0.0599	0.9344	0.9461	0.9402	0.9858

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1