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phibert-finetuned-ner – AI Model by girinlp-i2i | AlphaNeural AI
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transformers
pytorch
tensorboard
bert
token-classification
generated_from_trainer
autotrain_compatible
endpoints_compatible
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phibert-finetuned-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: 0.0293
Precision: 0.9238
Recall: 0.9213
F1: 0.9226
Accuracy: 0.9950
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: 8
eval_batch_size: 8
seed: 42
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
0.0309
1.0
5728
0.0305
0.8977
0.9042
0.9009
0.9939
0.0131
2.0
11456
0.0308
0.9089
0.9114
0.9102
0.9939
0.008
3.0
17184
0.0293
0.9238
0.9213
0.9226
0.9950
Framework versions
Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2
