Instructions to use matthewfarant/indobert-fertilizer-matching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matthewfarant/indobert-fertilizer-matching with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="matthewfarant/indobert-fertilizer-matching")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("matthewfarant/indobert-fertilizer-matching") model = AutoModelForSequenceClassification.from_pretrained("matthewfarant/indobert-fertilizer-matching") - Notebooks
- Google Colab
- Kaggle
This model is a fine-tuned version of IndoBERT Base Model (phase1 - uncased). Citation:
@inproceedings{wilie2020indonlu,
title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti},
booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
year={2020}
}
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