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:
- f43b5892f9ac87d2efc7ac5e7e6c39e4476d02ec5f6e5a244b7a030daf20b856
- Size of remote file:
- 2.33 GB
- SHA256:
- b6d473993385534b408edc3e0e77b6457e440e9d356c5883835b7ebb73ae5dde
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