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ner_model – AI Model by Patrick2000 | AlphaNeural AI

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Patrick2000/
ner_model

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token-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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ner_model

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

eval_loss: 1.0047
eval_model_preparation_time: 0.0051
eval_precision: 0.0553
eval_recall: 0.1916
eval_f1: 0.0858
eval_accuracy: 0.6607
eval_runtime: 4.1414
eval_samples_per_second: 227.217
eval_steps_per_second: 28.493
step: 0

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: 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

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1