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janbakker
/
leddoc-thesis

Transformers
PyTorch
led
text2text-generation
Model card Files Files and versions
xet
Community
1

Instructions to use janbakker/leddoc-thesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use janbakker/leddoc-thesis with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("janbakker/leddoc-thesis")
    model = AutoModelForSeq2SeqLM.from_pretrained("janbakker/leddoc-thesis")
  • Notebooks
  • Google Colab
  • Kaggle
leddoc-thesis
1.3 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
e1778ab verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • added_tokens.json
    201 Bytes
    Upload tokenizer almost 2 years ago
  • config.json
    3.65 kB
    Upload LEDForConditionalGeneration almost 2 years ago
  • generation_config.json
    163 Bytes
    Upload LEDForConditionalGeneration almost 2 years ago
  • merges.txt
    456 kB
    Upload tokenizer almost 2 years ago
  • model.safetensors
    648 MB
    xet
    Adding `safetensors` variant of this model over 1 year ago
  • pytorch_model.bin
    648 MB
    xet
    Upload LEDForConditionalGeneration almost 2 years ago
  • special_tokens_map.json
    957 Bytes
    Upload tokenizer almost 2 years ago
  • tokenizer.json
    2.11 MB
    Upload tokenizer almost 2 years ago
  • tokenizer_config.json
    1.34 kB
    Upload tokenizer almost 2 years ago
  • vocab.json
    798 kB
    Upload tokenizer almost 2 years ago