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Medical_NER_Testing – AI Model by Suramya | AlphaNeural AI
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Suramya
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Medical_NER_Testing
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transformers
tensorboard
safetensors
bert
token-classification
generated_from_trainer
google-bert/bert-base-uncased
finetune
apache-2.0
autotrain_compatible
endpoints_compatible
us
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Medical_NER_Testing
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: 1.2882
Precision: 0.3872
Recall: 0.4344
F1: 0.4095
Accuracy: 0.5465
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: 2
Training results
Training Loss
Epoch
Step
Validation Loss
Precision
Recall
F1
Accuracy
No log
1.0
35
1.5139
0.3408
0.4493
0.3876
0.4998
No log
2.0
70
1.2882
0.3872
0.4344
0.4095
0.5465
Framework versions
Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1
