Instructions to use dafqi/IndoBERT-base-p2-Text-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dafqi/IndoBERT-base-p2-Text-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dafqi/IndoBERT-base-p2-Text-Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dafqi/IndoBERT-base-p2-Text-Classification") model = AutoModelForSequenceClassification.from_pretrained("dafqi/IndoBERT-base-p2-Text-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dafqi/IndoBERT-base-p2-Text-Classification: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/dafqi/IndoBERT-base-p2-Text-Classification/resolve/main/training_args.bin
- Command line
-
hf download hf://dafqi/IndoBERT-base-p2-Text-Classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dafqi/IndoBERT-base-p2-Text-Classification/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- f9e60699791c611030dd205b63c3bd0577b8b2542bfa69f5f3ced9ba7e0cfcdd
- Size of remote file:
- 4.98 kB
- SHA256:
- c007f2afa00ecd10a148ca02c5257e176a581826a408a637c8a2e24391fa97e4
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