Summarization
Transformers
PyTorch
Safetensors
Chinese
t5
text2text-generation
text-generation-inference
Instructions to use twwch/mt5-base-summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use twwch/mt5-base-summary 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="twwch/mt5-base-summary")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("twwch/mt5-base-summary") model = AutoModelForSeq2SeqLM.from_pretrained("twwch/mt5-base-summary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f51f5abe9ca552c8246a71ebd15b7ed3daad1e664b276c7a61f5eab53acf9067
- Size of remote file:
- 2.33 GB
- SHA256:
- 4c8bfe0aae8da9bf7e71e494a24de0c6c861ffa241e362d9e841d5a1f8c43ccf
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