Nonzerophilip/bert-finetuned-ner_swedish_small_set_health_and_standart

Named Entity Recognition model for swedish

This model is a fine-tuned version of KBLab/bert-base-swedish-cased-nerfor only Swedish. It has been fine-tuned on the concatenation of a smaller version of SUC 3.0 and some medical text from the Swedish website 1177.

The model will predict the following entities:

Tag	Name	Exampel
PER	Person	(e.g., Johan and Sofia)
LOC	Location	(e.g., Göteborg and Spanien)
ORG	Organisation	(e.g., Volvo and Skatteverket) \
PHARMA_DRUGS	Medication	(e.g., Paracetamol and Omeprazol)
HEALTH	Illness/Diseases	(e.g., Cancer, sjuk and diabetes)
Relation	Family members	(e.g., Mamma and Farmor)

bert-finetuned-ner_swedish_small_set_health_and_standart

It achieves the following results on the evaluation set:

• Loss: 0.0963
• Precision: 0.7548
• Recall: 0.7811
• F1: 0.7677
• Accuracy: 0.9756

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
No log	1.0	219	0.1123	0.7674	0.6567	0.7078	0.9681
No log	2.0	438	0.0934	0.7643	0.7662	0.7652	0.9738
0.1382	3.0	657	0.0963	0.7548	0.7811	0.7677	0.9756

Framework versions

• Transformers 4.19.3
• Pytorch 1.7.1
• Datasets 2.2.2
• Tokenizers 0.12.1