Instructions to use sarakolding/daT5-summariser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sarakolding/daT5-summariser 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="sarakolding/daT5-summariser")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sarakolding/daT5-summariser") model = AutoModelForSeq2SeqLM.from_pretrained("sarakolding/daT5-summariser") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbfa1277167d00d4701be5527c0e9f9164477c7176f04a41ae2ad5ed0462590e
- Size of remote file:
- 1.95 GB
- SHA256:
- fc91e2fa415ded95ce0b768fcf744fde086c771a2610643122ac4a93e015b460
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