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bert-base-NER-finetuned-ner-ISU – AI Model by mcdzwil | AlphaNeural AI
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mcdzwil
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bert-base-NER-finetuned-ner-ISU
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transformers
pytorch
tensorboard
bert
token-classification
generated_from_trainer
mit
autotrain_compatible
endpoints_compatible
us
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bert-base-NER-finetuned-ner-ISU
This model is a fine-tuned version of
dslim/bert-base-NER
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1090
Precision: 0.9408
Recall: 0.8223
F1: 0.8776
Accuracy: 0.9644
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: 16
seed: 42
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
No log
1.0
48
0.1411
0.8970
0.7840
0.8367
0.9473
No log
2.0
96
0.1231
0.9453
0.7964
0.8645
0.9589
No log
3.0
144
0.1090
0.9408
0.8223
0.8776
0.9644
Framework versions
Transformers 4.17.0
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.6
