Summarization
Transformers
TensorBoard
Safetensors
English
t5
text2text-generation
summarizer
text summarization
abstractive summarization
text-generation-inference
Instructions to use KipperDev/t5_summarizer_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KipperDev/t5_summarizer_model 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="KipperDev/t5_summarizer_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KipperDev/t5_summarizer_model") model = AutoModelForSeq2SeqLM.from_pretrained("KipperDev/t5_summarizer_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from KipperDev/t5_summarizer_model: direct link, hf CLI and curl.
- Browser
- Download file 2.42 MB
-
https://huggingface.co/KipperDev/t5_summarizer_model/resolve/7feee5f3cb8a6d4b63b85be49c403e7bdd10da21/tokenizer.json
- Command line
-
hf download hf://KipperDev/t5_summarizer_model@7feee5f3cb8a6d4b63b85be49c403e7bdd10da21/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/KipperDev/t5_summarizer_model/resolve/7feee5f3cb8a6d4b63b85be49c403e7bdd10da21/tokenizer.json
2.42 MB
File too large to display, you can check the raw version instead.