ner-cdr-finetuned

rinrikatoki/ner-cdr-finetuned

ner-cdr-finetuned is a machine learning model from rinrikatoki. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

should probably proofread and complete it, then remove this comment. --

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At a glance

Model type: bert
Access: Public
Created: Jun 4, 2025
Updated: Jun 4, 2025
SHA: 72f47df9

Base models

dmis-lab/biobert-base-cased-v1.1

Model card

From the Hugging Face model README

ner-cdr-finetuned

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1871
Chemical: {'precision': 0.9215938303341902, 'recall': 0.9263565891472868, 'f1': 0.9239690721649483, 'number': 774}
Disease: {'precision': 0.7865546218487395, 'recall': 0.8327402135231317, 'f1': 0.8089887640449438, 'number': 562}
Overall Precision: 0.8631
Overall Recall: 0.8870
Overall F1: 0.8749
Overall Accuracy: 0.9480

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Chemical	Disease	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	57	0.2923	{'precision': 0.8410757946210269, 'recall': 0.8888888888888888, 'f1': 0.864321608040201, 'number': 774}	{'precision': 0.5264483627204031, 'recall': 0.7437722419928826, 'f1': 0.616519174041298, 'number': 562}	0.6861	0.8278	0.7503	0.8980
No log	2.0	114	0.2079	{'precision': 0.9134487350199734, 'recall': 0.8863049095607235, 'f1': 0.8996721311475411, 'number': 774}	{'precision': 0.7549909255898367, 'recall': 0.7402135231316725, 'f1': 0.7475292003593891, 'number': 562}	0.8464	0.8249	0.8355	0.9339
No log	3.0	171	0.1910	{'precision': 0.9088575096277278, 'recall': 0.9147286821705426, 'f1': 0.9117836445589182, 'number': 774}	{'precision': 0.7578125, 'recall': 0.8629893238434164, 'f1': 0.8069883527454244, 'number': 562}	0.8407	0.8930	0.8661	0.9464
No log	4.0	228	0.1856	{'precision': 0.9296875, 'recall': 0.9224806201550387, 'f1': 0.9260700389105059, 'number': 774}	{'precision': 0.770764119601329, 'recall': 0.8256227758007118, 'f1': 0.7972508591065292, 'number': 562}	0.8599	0.8817	0.8707	0.9461
No log	5.0	285	0.1871	{'precision': 0.9215938303341902, 'recall': 0.9263565891472868, 'f1': 0.9239690721649483, 'number': 774}	{'precision': 0.7865546218487395, 'recall': 0.8327402135231317, 'f1': 0.8089887640449438, 'number': 562}	0.8631	0.8870	0.8749	0.9480

Framework versions

Transformers 4.41.1
Pytorch 2.7.0+cu118
Datasets 3.6.0
Tokenizers 0.19.1

Type: bert
Created: Jun 4, 2025
Updated: Jun 4, 2025