Named Entity Recognition based on FERNET-CC_sk

This model is a fine-tuned version of fav-kky/FERNET-CC_sk on the Slovak wikiann dataset. It achieves the following results on the evaluation set:

Loss: 0.1763
Precision: 0.9360
Recall: 0.9472
F1: 0.9416
Accuracy: 0.9789

Intended uses & limitation

Supported classes: LOCATION, PERSON, ORGANIZATION

from transformers import pipeline

ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner')
input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké."
classifications = ner_pipeline(input_sentence)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 24
eval_batch_size: 24
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1259	1.0	834	0.1095	0.8963	0.9182	0.9071	0.9697
0.071	2.0	1668	0.0974	0.9270	0.9357	0.9313	0.9762
0.0323	3.0	2502	0.1259	0.9257	0.9330	0.9293	0.9745
0.0175	4.0	3336	0.1347	0.9241	0.9360	0.9300	0.9756
0.0156	5.0	4170	0.1407	0.9337	0.9404	0.9370	0.9780
0.0062	6.0	5004	0.1522	0.9267	0.9410	0.9338	0.9774
0.0055	7.0	5838	0.1559	0.9322	0.9429	0.9375	0.9780
0.0024	8.0	6672	0.1733	0.9321	0.9438	0.9379	0.9779
0.0009	9.0	7506	0.1765	0.9347	0.9468	0.9407	0.9784
0.0002	10.0	8340	0.1763	0.9360	0.9472	0.9416	0.9789

Framework versions

Transformers 4.14.0.dev0
Pytorch 1.10.0
Datasets 1.16.1
Tokenizers 0.10.3

crabz
/

FERNET-CC_sk-ner