Slovakbert Ner

🚀 Named Entity Recognition based on SlovakBERT

This model is a fine - tuned version of gerulata/slovakbert on the Slovak wikiann dataset. It can effectively perform named entity recognition tasks, providing high - precision results for identifying entities such as locations, persons, and organizations.

🚀 Quick Start

This model is a fine - tuned version of gerulata/slovakbert on the Slovak wikiann dataset. It achieves the following results on the evaluation set:

• Loss: 0.1600
• Precision: 0.9327
• Recall: 0.9470
• F1: 0.9398
• Accuracy: 0.9785

✨ Features

Supported classes: LOCATION, PERSON, ORGANIZATION

💻 Usage Examples

Basic Usage

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)

Advanced Usage

import spacy
from spacy import displacy

ner_map = {0: '0', 1: 'B-OSOBA', 2: 'I-OSOBA', 3: 'B-ORGANIZÁCIA', 4: 'I-ORGANIZÁCIA', 5: 'B-LOKALITA', 6: 'I-LOKALITA'}

entities = []
for i in range(len(classifications)):
    if classifications[i]['entity'] != 0:
        if ner_map[classifications[i]['entity']][0] == 'B':
            j = i + 1
            while j < len(classifications) and ner_map[classifications[j]['entity']][0] == 'I':
                j += 1
            entities.append((ner_map[classifications[i]['entity']].split('-')[1], classifications[i]['start'],
                             classifications[j - 1]['end']))

nlp = spacy.blank("en")  # it should work with any language

doc = nlp(input_sentence)

ents = []
for ee in entities:
    ents.append(doc.char_span(ee[1], ee[2], ee[0]))

doc.ents = ents

options = {"ents": ["OSOBA", "ORGANIZÁCIA", "LOKALITA"],
           "colors": {"OSOBA": "lightblue", "ORGANIZÁCIA": "lightcoral", "LOKALITA": "lightgreen"}}
displacy_html = displacy.render(doc, style="ent", options=options)

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e - 05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 15.0

Training results

Framework versions

• Transformers 4.13.0.dev0
• Pytorch 1.10.0+cu113
• Datasets 1.15.1
• Tokenizers 0.10.3

📄 License

This project is under the MIT license.