Summarization
Transformers
Safetensors
Indonesian
bart
text2text-generation
samsum
indonesia
seq2seq
finetuning
Instructions to use bitong/bart_finetuning_samsum_indo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bitong/bart_finetuning_samsum_indo with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="bitong/bart_finetuning_samsum_indo")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bitong/bart_finetuning_samsum_indo") model = AutoModelForSeq2SeqLM.from_pretrained("bitong/bart_finetuning_samsum_indo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97d866b41a1246b0337c1b06e5fd32e6276c8f590e133990578016fa5c6ab5a5
- Size of remote file:
- 5.3 kB
- SHA256:
- f71d9b6e4a03f8c982cababf9c42618f60c42d846b3d1e87a98e7a531940a60f
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