Instructions to use yhavinga/mt5-base-cnn-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yhavinga/mt5-base-cnn-nl 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="yhavinga/mt5-base-cnn-nl")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("yhavinga/mt5-base-cnn-nl") model = AutoModelForSeq2SeqLM.from_pretrained("yhavinga/mt5-base-cnn-nl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9eac42fe52af7eaaaad879470d0262ccf39525ddc790e3bb186b4c3e865223b
- Size of remote file:
- 2.48 kB
- SHA256:
- 7450c852c6612e948a47736de68c91a592c1e37ae3a0689c0bcfd7eb58b814c1
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