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BioBERT_BioNLP13CG_NER – AI Model by judithrosell | AlphaNeural AI
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judithrosell
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BioBERT_BioNLP13CG_NER
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transformers
tensorboard
safetensors
bert
token-classification
generated_from_trainer
dmis-lab/biobert-v1.1
finetune
autotrain_compatible
endpoints_compatible
us
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BioBERT_BioNLP13CG_NER
This model is a fine-tuned version of
dmis-lab/biobert-v1.1
on the None dataset. It achieves the following results on the evaluation set:
Loss: 0.1954
Precision: 0.8710
Recall: 0.8602
F1: 0.8656
Accuracy: 0.9540
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
0.3032
0.8114
0.7836
0.7973
0.9291
No log
2.0
191
0.2073
0.8548
0.8532
0.8540
0.9498
No log
2.98
285
0.1954
0.8710
0.8602
0.8656
0.9540
Framework versions
Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0
