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, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("KipperDev/t5_summarizer_model") model = AutoModelForSeq2SeqLM.from_pretrained("KipperDev/t5_summarizer_model") - Notebooks
- Google Colab
- Kaggle
t5_summarizer_model / runs /Jan29_20-10-43_b5b17a66f70d /events.out.tfevents.1706559076.b5b17a66f70d.311.0
- Xet hash:
- 2e2a784f15c629f55e15d699ac28a3ac15475d51b57986aa72140568f36a3a16
- Size of remote file:
- 6.2 kB
- SHA256:
- a5d549d1021d25dc765692d103461f9377bfa5f0b705ce1bdeda8a8dd6176e84
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