SportExtract NER Model

Model Description

This is a Named Entity Recognition (NER) model fine-tuned on Indonesian sports news articles, specifically for football/soccer content.

Base Model: IndoBERT (indobenchmark/indobert-base-p1)

Model Type: Multi-label token classification

Entities Detected

The model can detect the following entities in Indonesian sports articles:

• ATLET - Athletes/Players
• TIM - Teams
• ORGANISASI - Organizations
• KEWARGANEGARAAN - Nationality
• POSISI - Player positions
• UMUR - Age
• AKSI - Actions in matches
• PENGHARGAAN - Awards/achievements
• STATISTIK - Statistics
• SKOR - Match scores
• TANGGAL - Dates
• STADION - Stadiums
• KEJUARAAN - Tournaments/competitions
• ALASAN_PERISTIWA - Event reasons/context

Usage

import torch
from transformers import AutoTokenizer, AutoModel
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="george121212afasf/model",
    filename="best_model.pt"
)

# Load checkpoint
checkpoint = torch.load(model_path, map_location='cpu')

# Get tokenizer
tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-base-p1")

# Your model class and inference code here

Training Data

Trained on annotated Indonesian sports news articles from various sources.

Model Size

• Parameters: ~125M (IndoBERT base)
• File size: ~1420 MB

Intended Use

This model is designed for extracting sports-related entities from Indonesian news articles, particularly for:

• Sports journalism analysis
• Automated content tagging
• Information extraction from sports news
• 5W1H (Who, What, When, Where, Why, How) analysis

Limitations

• Optimized for Indonesian language sports content
• Best performance on football, basketball, and badminton articles
• May not generalize well to other sports domains

Contact

For questions or feedback, please open an issue in the repository.