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")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("KipperDev/t5_summarizer_model") model = AutoModelForMultimodalLM.from_pretrained("KipperDev/t5_summarizer_model") - Notebooks
- Google Colab
- Kaggle
t5_summarizer_model / runs /Jan25_16-27-22_366b157464d4 /events.out.tfevents.1706200053.366b157464d4.2971.0
- Xet hash:
- 9665474606ecb00088da22d21f74fabd1f04678b1d4dac23526af1a46884ed0e
- Size of remote file:
- 12 kB
- SHA256:
- 3f296f886a9dc99a1b574888b16f7967bd9cfb571735c125ed915aa7e806c30c
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