Instructions to use EducativeCS2023/bart-base-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EducativeCS2023/bart-base-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("EducativeCS2023/bart-base-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("EducativeCS2023/bart-base-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- afe2e858d1c19e1a131bfe637fb76dcc95d6956713d65a066beb0ca7ee282e35
- Size of remote file:
- 558 MB
- SHA256:
- d6c8dd29732a86a9ebe51d61d35240566ff86acc689c8cd99c4c96a6bd393d73
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