Summarization
Transformers
PyTorch
Safetensors
Enawené-Nawé
t5
text2text-generation
Trained with AutoTrain
text-generation-inference
Instructions to use chiakya/T5-base-Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chiakya/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="chiakya/T5-base-Summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("chiakya/T5-base-Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("chiakya/T5-base-Summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2786e5a810bb110a403fe5c7767fd6d3191c8ead2cb4eb0cdaf487ab91546a7d
- Size of remote file:
- 2.42 MB
- SHA256:
- 8723bf9b53cfe5d55721bc95ee269271918dc826aa480a8676db0680883d9495
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