Instructions to use silmi224/finetune-led-thousanddata with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmi224/finetune-led-thousanddata 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="silmi224/finetune-led-thousanddata")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmi224/finetune-led-thousanddata") model = AutoModelForSeq2SeqLM.from_pretrained("silmi224/finetune-led-thousanddata") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1444e17b5b1c688ecf6b5e054217c512b0e05cd47b3a94b23d5d9a59b1c8b80
- Size of remote file:
- 4.73 kB
- SHA256:
- fe46a1354b4d0462227a3d376d978ff5a959aa38698df130a651bf4dcc2a6376
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