PROCIT-SANDBOX
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training_ner_0.0

Token Classification

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NER_training_base_uncased_with_randomization

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

Loss: 0.0472
Precision: 0.9550
Recall: 0.9576
F1: 0.9563
Accuracy: 0.9849

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: 32
seed: 12
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0508	1.0	9836	0.0472	0.9550	0.9576	0.9563	0.9849
0.035	2.0	19672	0.0473	0.9590	0.9644	0.9617	0.9870
0.021	3.0	29508	0.0537	0.9592	0.9636	0.9614	0.9870

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

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Model size

109M params

Tensor type

F32

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Token Classification

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Base model

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Evaluation results

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