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(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:

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]

See the gist for the source code to used to train the model:


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