scibert-base-uncased-ner-tuning

nattkorat/scibert-base-uncased-ner-tuning

scibert-base-uncased-ner-tuning is a token classification model from nattkorat. Use it when you need labels on individual words, such as names. It is set up for transformers.

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

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At a glance

Task: Token Classification
Library: transformers
Model type: bert
Access: Public
Created: May 13, 2025
Updated: May 13, 2025
SHA: b03f21ab

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Base models

allenai/scibert_scivocab_uncased

Model card

From the Hugging Face model README

scibert-base-uncased-ner-tuning

This model is a fine-tuned version of allenai/scibert_scivocab_uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0055
Cases: {'precision': 1.0, 'recall': 0.9984615384615385, 'f1': 0.9992301770592764, 'number': 650}
Country: {'precision': 0.9986824769433466, 'recall': 0.9973684210526316, 'f1': 0.9980250164581962, 'number': 760}
Date: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 109}
Deaths: {'precision': 0.9978260869565218, 'recall': 0.9956616052060737, 'f1': 0.996742671009772, 'number': 461}
Virus: {'precision': 0.998995983935743, 'recall': 0.9979939819458375, 'f1': 0.9984947315604615, 'number': 997}
Overall Precision: 0.9990
Overall Recall: 0.9976
Overall F1: 0.9983
Overall Accuracy: 0.9992

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Cases	Country	Date	Deaths	Virus	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
No log	1.0	291	0.0042	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 650}	{'precision': 0.9986824769433466, 'recall': 0.9973684210526316, 'f1': 0.9980250164581962, 'number': 760}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 109}	{'precision': 1.0, 'recall': 0.9978308026030369, 'f1': 0.998914223669924, 'number': 461}	{'precision': 0.9969727547931383, 'recall': 0.9909729187562688, 'f1': 0.993963782696177, 'number': 997}	0.9987	0.9960	0.9973	0.9990
0.0545	2.0	582	0.0055	{'precision': 1.0, 'recall': 0.9953846153846154, 'f1': 0.9976869699306091, 'number': 650}	{'precision': 0.9986824769433466, 'recall': 0.9973684210526316, 'f1': 0.9980250164581962, 'number': 760}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 109}	{'precision': 0.9935344827586207, 'recall': 1.0, 'f1': 0.9967567567567568, 'number': 461}	{'precision': 0.9979879275653923, 'recall': 0.9949849548645938, 'f1': 0.9964841788046208, 'number': 997}	0.9980	0.9966	0.9973	0.9990
0.0545	3.0	873	0.0071	{'precision': 1.0, 'recall': 0.9969230769230769, 'f1': 0.9984591679506933, 'number': 650}	{'precision': 0.9986824769433466, 'recall': 0.9973684210526316, 'f1': 0.9980250164581962, 'number': 760}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 109}	{'precision': 0.9956331877729258, 'recall': 0.9891540130151844, 'f1': 0.9923830250272034, 'number': 461}	{'precision': 0.9979879275653923, 'recall': 0.9949849548645938, 'f1': 0.9964841788046208, 'number': 997}	0.9983	0.9953	0.9968	0.9987
0.0035	4.0	1164	0.0051	{'precision': 1.0, 'recall': 0.9984615384615385, 'f1': 0.9992301770592764, 'number': 650}	{'precision': 0.9986824769433466, 'recall': 0.9973684210526316, 'f1': 0.9980250164581962, 'number': 760}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 109}	{'precision': 0.9978260869565218, 'recall': 0.9956616052060737, 'f1': 0.996742671009772, 'number': 461}	{'precision': 0.998995983935743, 'recall': 0.9979939819458375, 'f1': 0.9984947315604615, 'number': 997}	0.9990	0.9976	0.9983	0.9992
0.0035	5.0	1455	0.0055	{'precision': 1.0, 'recall': 0.9984615384615385, 'f1': 0.9992301770592764, 'number': 650}	{'precision': 0.9986824769433466, 'recall': 0.9973684210526316, 'f1': 0.9980250164581962, 'number': 760}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 109}	{'precision': 0.9978260869565218, 'recall': 0.9956616052060737, 'f1': 0.996742671009772, 'number': 461}	{'precision': 0.998995983935743, 'recall': 0.9979939819458375, 'f1': 0.9984947315604615, 'number': 997}	0.9990	0.9976	0.9983	0.9992

Framework versions

Transformers 4.51.3
Pytorch 2.5.1+cu121
Datasets 3.5.0
Tokenizers 0.21.1

Task: Token Classification
Library: transformers
Type: bert
Created: May 13, 2025
Updated: May 13, 2025