Text Classification
Transformers
Safetensors
Indonesian
bert
natural-language-inference
indonesian
perturbation-robustness
text-embeddings-inference
Instructions to use fabhiansan/indoBERT-Large-FactChecking-Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fabhiansan/indoBERT-Large-FactChecking-Summarization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fabhiansan/indoBERT-Large-FactChecking-Summarization")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fabhiansan/indoBERT-Large-FactChecking-Summarization") model = AutoModelForSequenceClassification.from_pretrained("fabhiansan/indoBERT-Large-FactChecking-Summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from fabhiansan/indoBERT-Large-FactChecking-Summarization: direct link, hf CLI and curl.
- Browser
- Download file 2.68 GB
-
https://huggingface.co/fabhiansan/indoBERT-Large-FactChecking-Summarization/resolve/main/optimizer.pt
- Command line
-
hf download hf://fabhiansan/indoBERT-Large-FactChecking-Summarization/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/fabhiansan/indoBERT-Large-FactChecking-Summarization/resolve/main/optimizer.pt
2.68 GB
- Xet hash:
- 4ac12c0aaef485a5173155f99cdec919b238e5211ee0a5c709e825c3786f9568
- Size of remote file:
- 2.68 GB
- SHA256:
- 672b9970ac6e4347a4a88e73c4549d12033090d32674454c9d8e7bb7a4e4950f
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