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 /Jan13_13-35-25_8838525cb97c /events.out.tfevents.1705152941.8838525cb97c.1886.3
- Xet hash:
- a4c8c4739e462b02d3786158ba5ee69d4ba369927a80263ee34eab2f0604abd3
- Size of remote file:
- 9.25 kB
- SHA256:
- 54ecd6c69629f44391c4e98665859e8a90fc55ae2c2355a949545362d5236c6f
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