readme: ---
  language: en
  thumbnail: url to a thumbnail used in social sharing
  tags:
  - array
  - of
  - tags
  widget:
  - text: "question: which description describes the word \" java \" best in the following\
      \ context? descriptions: [  \" A drink consisting of an infusion of ground coffee\
      \ beans \" ,  \" a platform-independent programming lanugage \" ,  or \" an island\
      \ in Indonesia to the south of Borneo \" ]  context: I like to drink ' java '\
      \ in the morning ."
  ---
  
  # T5-large for Word Sense Disambiguation
  
  If you are using this model in your research work, please cite
  
  ```bib
  @article{wahle2021incorporating,
    title={Incorporating Word Sense Disambiguation in Neural Language Models},
    author={Wahle, Jan Philip and Ruas, Terry and Meuschke, Norman and Gipp, Bela},
    journal={arXiv preprint arXiv:2106.07967},
    year={2021}
  }
  ```
  
  This is the checkpoint for T5-large after being trained on the [SemCor 3.0 dataset](http://lcl.uniroma1.it/wsdeval/).
  
  Additional information about this model:
  
  * [The t5-large model page](https://huggingface.co/t5-large)
  * [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf)
  * [Official implementation by Google](https://github.com/google-research/text-to-text-transfer-transformer)
  
  The model can be loaded to perform a few-shot classification like so:
  
  ```py
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
  
  model = AutoModelForSeq2SeqLM.from_pretrained("jpelhaw/t5-word-sense-disambiguation")
  tokenizer = AutoTokenizer.from_pretrained("jpelhaw/t5-word-sense-disambiguation")
  
  input = '''question: which description describes the word " java "\
             best in the following context? \
  descriptions:[  " A drink consisting of an infusion of ground coffee beans ", 
                  " a platform-independent programming language ", or
                  " an island in Indonesia to the south of Borneo " ] 
  context: I like to drink " java " in the morning .'''
  
  
  example = tokenizer.tokenize(input, add_special_tokens=True)
  
  answer = model.generate(input_ids=example['input_ids'], 
                                  attention_mask=example['attention_mask'], 
                                  max_length=135)
                                  
  # "a drink consisting of an infusion of ground coffee beans"
  ```
  