wuriyanto

ner-bert-indonesian-v1 is a fine-tuned google-bert/bert-base-multilingual-uncased which is used for named-entity-recognition (NER) tasks in Indonesian. In version 1, the model is quite good at recognizing the following 4 entity types: - 0 others (entities not yet recognized by the model) - Lainnya - Person - Orang - Organisation - Organisasi - Place - Tempat/Lokasi The DEE NER dataset: Ika Alfina, Ruli Manurung, and Mohamad Ivan Fanany, "DBpedia Entities Expansion in Automatically Building Dataset for Indonesian NER", in Proceeding of 8th International Conference on Advanced Computer Science and Information Systems 2016 (ICACSIS 2016). The MDEE and Singgalang NER dataset: Ika Alfina, Septiviana Savitri, and Mohamad Ivan Fanany, "Modified DBpedia Entities Expansion for Tagging Automatically NER Dataset", in Proceeding of 9th International Conference on Advanced Computer Science and Information Systems 2017 (ICACSIS 2017). The Gold Standard: Andry Luthfi, Bayu Distiawan, and Ruli Manurung, "Building an Indonesian named entity recognizer using Wikipedia and DBPedia", in the Proceesing of 2014 International Conference on Asian Language Processing (IALP 2014).

This model uses the `sentence-transformers/paraphrase-multilingual-mpnet-base-v2` base model, which is trained with the Indonesian sentiment dataset. In v1, this model can predict 2 sentiments. Namely `baik` and `buruk` sentiments.

🚘 YOLOv8 model for detecting Indonesian vehicle license plates. This model was trained using the Indonesian Plate Number from Multi Sources dataset. - Architecture: YOLOv8 (Ultralytics) - Task: Object Detection - Classes: 1 (plate) - Epochs: 50 - Image size: 640 - Framework: Ultralytics Here’s an example detection result (bounding box around the plate): Image for demonstration purposes only. Source: Google/ https://tirto.id/cara-mencari-lokasi-uji-emisi-kendaraan-di-jakarta-secara-online-f8ZU - Dataset: Indonesian Plate Number from Multi Sources (Kaggle https://www.kaggle.com/datasets/linkgish/indonesian-plate-number-from-multi-sources) - Train/Val split already provided. This model is released under the MIT License (feel free to modify).