readme: ---
  language: en
  widget:
   - text: 'define "toecoin": toecoin rose by 200% after Elon Musk mentioned it in his tweet'
  datasets:
  - 'marksverdhei/wordnet-definitions-en-2021'
  ---
  
  # T5-define  
  
  (This model is still a work in progress. If you use it for fine tuning, make sure to save a local copy)
  
  This model is trained to generate word definitions based on the word and a context,
  using a subset of wordnet for all words that have an example and definition.
  The model uses task prompts on the format 'define "[word]": [example sentence]'
  
  This model in particular is a one-shot learner for unseen words, as it has to infer the definition by only one example
  
  How to run:
  ```python
  from transformers import T5ForConditionalGeneration, T5Tokenizer
  
  tokenizer = T5Tokenizer.from_pretrained("marksverdhei/t5-base-define")
  model = T5ForConditionalGeneration.from_pretrained("marksverdhei/t5-base-define")
  
  prompt = "define \"noseplow\": The children hid as the noseplow drove across the street"
  
  ids = tokenizer(prompt, return_tensors="pt").input_ids
  generated_tokens = model.generate(ids)[0][1:-1]
  print(tokenizer.decode(generated_tokens))
  ```
  
  See the gist for the source code to used to train the model:
  
  https://gist.github.com/marksverdhei/0a13f67e65460b71c05fcf558a6a91ae