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ClinicalBERT_JNLPBA_NER – AI Model by judithrosell | AlphaNeural AI
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judithrosell
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ClinicalBERT_JNLPBA_NER
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transformers
tensorboard
safetensors
distilbert
token-classification
generated_from_trainer
medicalai/ClinicalBERT
finetune
autotrain_compatible
endpoints_compatible
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ClinicalBERT_JNLPBA_NER
This model is a fine-tuned version of
medicalai/ClinicalBERT
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1713
Precision: 0.9452
Recall: 0.9354
F1: 0.9403
Accuracy: 0.9427
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
gradient_accumulation_steps: 2
total_train_batch_size: 32
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
0.3475
1.0
582
0.1914
0.9330
0.9314
0.9322
0.9358
0.1835
2.0
1164
0.1746
0.9426
0.9332
0.9379
0.9408
0.158
3.0
1746
0.1713
0.9452
0.9354
0.9403
0.9427
Framework versions
Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0
