Instructions to use cahya/bert-base-indonesian-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cahya/bert-base-indonesian-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="cahya/bert-base-indonesian-NER", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("cahya/bert-base-indonesian-NER") model = AutoModelForTokenClassification.from_pretrained("cahya/bert-base-indonesian-NER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Available Labels
#1
by fwe01 - opened
Can you please gave us some guidance for what each label means. for example "B-EVT" what does EVT mean ?
thankyou in advance
You can find the meaning of the labels here https://huggingface.co/datasets/id_nergrit_corpus
'CRD': Cardinal
'DAT': Date
'EVT': Event
'FAC': Facility
'GPE': Geopolitical Entity
'LAW': Law Entity (such as Undang-Undang)
'LOC': Location
'MON': Money
'NOR': Political Organization
'ORD': Ordinal
'ORG': Organization
'PER': Person
'PRC': Percent
'PRD': Product
'QTY': Quantity
'REG': Religion
'TIM': Time
'WOA': Work of Art
'LAN': Language
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