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AlgorithmicResearchGroup
/
led_base_16384_arxiv_summarization

Summarization
Transformers
PyTorch
Safetensors
English
led
text2text-generation
Eval Results (legacy)
Model card Files Files and versions
xet
Community
2

Instructions to use AlgorithmicResearchGroup/led_base_16384_arxiv_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use AlgorithmicResearchGroup/led_base_16384_arxiv_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="AlgorithmicResearchGroup/led_base_16384_arxiv_summarization")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("AlgorithmicResearchGroup/led_base_16384_arxiv_summarization")
    model = AutoModelForSeq2SeqLM.from_pretrained("AlgorithmicResearchGroup/led_base_16384_arxiv_summarization", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
led_base_16384_arxiv_summarization
2.59 GB
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  • 4 contributors
History: 6 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
dcce422 over 3 years ago
  • .gitattributes
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    Adding `safetensors` variant of this model over 3 years ago
  • README.md
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  • config.json
    1.28 kB
    first commit almost 4 years ago
  • merges.txt
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  • model.safetensors
    648 MB
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  • optimizer.pt
    1.29 GB
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  • pytorch_model.bin
    648 MB
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  • scaler.pt
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  • scheduler.pt
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • trainer_state.json
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  • training_args.bin
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  • vocab.json
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