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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ef3c2ccf2b4813ff3e7a79548fbd2e1a1eccd01b75e1389efbd6bca4e1d6584
- Size of remote file:
- 4.73 kB
- SHA256:
- 0bf978c42afa188a66276d6cfc13833ad742a5898dd6d5c4797a4eccb895015f
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