Xlm Roberta Base Romanian Ner Ronec

🚀 XLM - RoBERTa Romanian NER Model

An XLM - RoBERTa model trained on the Ronec dataset for named - entity recognition, achieving an F1 - Macro score of 95 on the test set.

🚀 Quick Start

The XLM - RoBERTa model is fine - tuned on the ronec dataset for named - entity recognition tasks.

✨ Features

• High Performance: Achieves an F1 - Macro score of 0.95 on the test set.
• Trained on Ronec: Utilizes the Ronec dataset for training, which is suitable for Romanian NER tasks.

📦 Installation

There is no specific installation step provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("EvanD/xlm-roberta-base-romanian-ner-ronec")
ner_model = AutoModelForTokenClassification.from_pretrained("EvanD/xlm-roberta-base-romanian-ner-ronec")

nlp = pipeline("ner", model=ner_model, tokenizer=tokenizer, aggregation_strategy="simple")
example = "Numele meu este Amadeus Wolfgang și locuiesc în Berlin"

ner_results = nlp(example)
print(ner_results)

# [
#     {
#         'entity_group': 'PER',
#         'score': 0.9966806,
#         'word': 'Amadeus Wolfgang',
#         'start': 16,
#         'end': 32
#     },
#     {'entity_group': 'GPE',
#      'score': 0.99694663,
#      'word': 'Berlin',
#      'start': 48,
#      'end': 54
#      }
# ]

📚 Documentation

Performance Metrics

Inference Parameters

• Aggregation Strategy: simple
• Grouped Entities: true

Pipeline Tag

• token - classification

Tags

• named - entity - recognition
• sequence - tagger - model

Widget Example

text: Numele meu este Amadeus Wolfgang și locuiesc în Berlin