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PubMedBERT_BioNLP13CG_NER – AI Model by judithrosell | AlphaNeural AI
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judithrosell
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PubMedBERT_BioNLP13CG_NER
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transformers
tensorboard
safetensors
bert
token-classification
generated_from_trainer
microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
finetune
mit
autotrain_compatible
endpoints_compatible
us
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PubMedBERT_BioNLP13CG_NER
This model is a fine-tuned version of
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.2094
Precision: 0.8591
Recall: 0.8404
F1: 0.8497
Accuracy: 0.9510
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
No log
0.99
95
0.3540
0.7748
0.7481
0.7612
0.9170
No log
2.0
191
0.2264
0.8430
0.8356
0.8393
0.9467
No log
2.98
285
0.2094
0.8591
0.8404
0.8497
0.9510
Framework versions
Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0
