Instructions to use marianna13/flan-t5-base-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marianna13/flan-t5-base-summarization 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="marianna13/flan-t5-base-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("marianna13/flan-t5-base-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("marianna13/flan-t5-base-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c8bde4dd8f8c98781e721a23e471033ac414eed5ab036f3201b8994d6eff18a0
- Size of remote file:
- 990 MB
- SHA256:
- 903e9f2118b8830751e8636c3630bce64994ed00d536974fdce2ff23c31ee2b0
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