Text Classification
Transformers
Safetensors
Indonesian
bert
natural-language-inference
indonesian
roberta
perturbation-robustness
text-embeddings-inference
Instructions to use fabhiansan/indoBERT-Base-FactChecking-Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fabhiansan/indoBERT-Base-FactChecking-Summarization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fabhiansan/indoBERT-Base-FactChecking-Summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fabhiansan/indoBERT-Base-FactChecking-Summarization") model = AutoModelForSequenceClassification.from_pretrained("fabhiansan/indoBERT-Base-FactChecking-Summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from fabhiansan/indoBERT-Base-FactChecking-Summarization: direct link, hf CLI and curl.
- Browser
- Download file 996 MB
-
https://huggingface.co/fabhiansan/indoBERT-Base-FactChecking-Summarization/resolve/main/optimizer.pt
- Command line
-
hf download hf://fabhiansan/indoBERT-Base-FactChecking-Summarization/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/fabhiansan/indoBERT-Base-FactChecking-Summarization/resolve/main/optimizer.pt
996 MB
- Xet hash:
- 6ca30c08444f7d8d83ee00d1daff52c573257f316d4af83c3ec0c9a9414e93a8
- Size of remote file:
- 996 MB
- SHA256:
- 4d3e3dfc819d6fae9c323f1323001ffae6add131dfe84dbddf2f57456e2f0e38
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