Biomed_bert-base-uncased-NER – AI Model by Pontonkid | AlphaNeural AI

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Pontonkid/
Biomed_bert-base-uncased-NER

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token-classification

generated_from_trainer

microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext

autotrain_compatible

endpoints_compatible

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checkpoint-1000

This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext on the ncbi_disease dataset. It achieves the following results on the evaluation set:

Loss: 0.0543
Precision: 0.8457
Recall: 0.8906
F1: 0.8676
Accuracy: 0.9851

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	340	0.0596	0.7778	0.875	0.8235	0.9795
0.0787	2.0	680	0.0416	0.8246	0.8865	0.8544	0.9851
0.0202	3.0	1020	0.0494	0.8385	0.8812	0.8593	0.9846
0.0202	4.0	1360	0.0543	0.8457	0.8906	0.8676	0.9851

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0