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imvladikon
/
het5_summarization

Summarization
Transformers
PyTorch
Safetensors
Hebrew
t5
text2text-generation
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use imvladikon/het5_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use imvladikon/het5_summarization 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="imvladikon/het5_summarization")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("imvladikon/het5_summarization")
    model = AutoModelForSeq2SeqLM.from_pretrained("imvladikon/het5_summarization")
  • Notebooks
  • Google Colab
  • Kaggle
het5_summarization
1.96 GB
Ctrl+K
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  • 2 contributors
History: 3 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
60ad81f almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • config.json
    721 Bytes
    init almost 3 years ago
  • generation_config.json
    112 Bytes
    init almost 3 years ago
  • model.safetensors
    977 MB
    xet
    Adding `safetensors` variant of this model almost 3 years ago
  • pytorch_model.bin
    977 MB
    xet
    init almost 3 years ago
  • special_tokens_map.json
    74 Bytes
    init almost 3 years ago
  • spiece.model
    746 kB
    xet
    init almost 3 years ago
  • tokenizer.json
    2.19 MB
    init almost 3 years ago
  • tokenizer_config.json
    285 Bytes
    init almost 3 years ago