metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - ju-bezdek/conll2003-SK-NER
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: outputs
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: ju-bezdek/conll2003-SK-NER
          type: ju-bezdek/conll2003-SK-NER
          args: conll2003-SK-NER
        metrics:
          - name: Precision
            type: precision
            value: 0.8189727994593682
          - name: Recall
            type: recall
            value: 0.8389581169955002
          - name: F1
            type: f1
            value: 0.8288450029922203
          - name: Accuracy
            type: accuracy
            value: 0.9526157920337243

outputs

This model is a fine-tuned version of gerulata/slovakbert on the ju-bezdek/conll2003-SK-NER dataset. It achieves the following results on the evaluation (validation) set:

Loss: 0.1752
Precision: 0.8190
Recall: 0.8390
F1: 0.8288
Accuracy: 0.9526

Model description

More information needed

Code example

from transformers import pipeline, AutoModel, AutoTokenizer
from spacy import displacy
import os


model_path="ju-bezdek/slovakbert-conll2003-sk-ner"

aggregation_strategy="max"
ner_pipeline = pipeline(task='ner', model=model_path, aggregation_strategy=aggregation_strategy)

input_sentence= "Ruský premiér Viktor Černomyrdin v piatok povedal, že prezident Boris Jeľcin , ktorý je na dovolenke mimo Moskvy , podporil mierový plán šéfa bezpečnosti Alexandra Lebedu pre Čečensko, uviedla tlačová agentúra Interfax"
ner_ents = ner_pipeline(input_sentence)
print(ner_ents)

ent_group_labels = [ner_pipeline.model.config.id2label[i][2:] for i in ner_pipeline.model.config.id2label if i>0]

options = {"ents":ent_group_labels}

dicplacy_ents = [{"start":ent["start"], "end":ent["end"], "label":ent["entity_group"]} for ent in ner_ents]
displacy.render({"text":input_sentence, "ents":dicplacy_ents}, style="ent", options=options, jupyter=True, manual=True)

Result:

Ruský MISC premiér Viktor Černomyrdin PER v piatok povedal, že prezident Boris Jeľcin, PER , ktorý je na dovolenke mimo Moskvy LOC , podporil mierový plán šéfa bezpečnosti Alexandra Lebedu PER pre Čečensko, LOC uviedla tlačová agentúra Interfax ORG

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3237	1.0	878	0.2541	0.7125	0.8059	0.7563	0.9283
0.1663	2.0	1756	0.2370	0.7775	0.8090	0.7929	0.9394
0.1251	3.0	2634	0.2289	0.7732	0.8029	0.7878	0.9385
0.0984	4.0	3512	0.2818	0.7294	0.8189	0.7715	0.9294
0.0808	5.0	4390	0.3138	0.7615	0.7900	0.7755	0.9326
0.0578	6.0	5268	0.3072	0.7548	0.8222	0.7871	0.9370
0.0481	7.0	6146	0.2778	0.7897	0.8156	0.8025	0.9408
0.0414	8.0	7024	0.3336	0.7695	0.8201	0.7940	0.9389
0.0268	9.0	7902	0.3294	0.7868	0.8140	0.8002	0.9409
0.0204	10.0	8780	0.3693	0.7657	0.8239	0.7938	0.9376
0.016	11.0	9658	0.3816	0.7932	0.8242	0.8084	0.9425
0.0108	12.0	10536	0.3607	0.7929	0.8256	0.8089	0.9431
0.0078	13.0	11414	0.3980	0.7915	0.8240	0.8074	0.9423
0.0062	14.0	12292	0.4096	0.7995	0.8247	0.8119	0.9436
0.0035	15.0	13170	0.4177	0.8006	0.8251	0.8127	0.9438

Framework versions

Transformers 4.15.0
Pytorch 1.10.1+cu102
Datasets 1.17.0
Tokenizers 0.10.3