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MK-5
/
t5-small-Abstractive-Summarizer

Summarization
Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use MK-5/t5-small-Abstractive-Summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use MK-5/t5-small-Abstractive-Summarizer 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="MK-5/t5-small-Abstractive-Summarizer")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("MK-5/t5-small-Abstractive-Summarizer")
    model = AutoModelForSeq2SeqLM.from_pretrained("MK-5/t5-small-Abstractive-Summarizer")
  • Notebooks
  • Google Colab
  • Kaggle
t5-small-Abstractive-Summarizer / runs
26.6 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 6 commits
MK-5's picture
MK-5
Training complete
3ad340d verified almost 2 years ago
  • Sep06_15-49-44_2c639ca3c197
    Training complete almost 2 years ago
  • Sep07_06-00-17_fc36e533de6d
    Training complete almost 2 years ago