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ner-bio-annotated-4 – AI Model by urbija | AlphaNeural AI
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urbija
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ner-bio-annotated-4
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transformers
pytorch
bert
token-classification
generated_from_trainer
apache-2.0
autotrain_compatible
endpoints_compatible
us
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ner-bio-annotated-4
This model is a fine-tuned version of
bert-base-uncased
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1253
Precision: 0.7316
Recall: 0.7846
F1: 0.7572
Accuracy: 0.9640
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: 0.0002
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
lr_scheduler_warmup_ratio: 0.2
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
No log
1.0
67
0.1690
0.5398
0.6195
0.5769
0.9422
No log
2.0
134
0.1422
0.6725
0.7493
0.7089
0.9562
No log
3.0
201
0.1253
0.7316
0.7846
0.7572
0.9640
Framework versions
Transformers 4.30.2
Pytorch 2.0.0+cpu
Datasets 2.1.0
Tokenizers 0.13.3
