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bert-finetuned-ner.default_parameters – AI Model by Mabel465 | AlphaNeural AI
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bert-finetuned-ner.default_parameters
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transformers
pytorch
bert
token-classification
generated_from_trainer
google-bert/bert-base-cased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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bert-finetuned-ner.default_parameters
This model is a fine-tuned version of
bert-base-cased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2075
Precision: 0.5645
Recall: 0.4450
F1: 0.4977
Accuracy: 0.9228
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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
No log
1.0
425
0.1828
0.5952
0.3553
0.4449
0.9136
0.1165
2.0
850
0.1917
0.5760
0.4127
0.4808
0.9181
0.0438
3.0
1275
0.2075
0.5645
0.4450
0.4977
0.9228
Framework versions
Transformers 4.34.1
Pytorch 2.1.0
Datasets 2.14.6
Tokenizers 0.14.1
