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@@ -15,9 +15,7 @@ The model uses task prompts on the format 'define "[word]": [example sentence]'
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  To my knowledge, this is the first public model trained on a word definition task.
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  Similar work: [Zero-shot Word Sense Disambiguation using Sense Definition Embeddings](https://aclanthology.org/P19-1568.pdf)
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- For this project, there are two objectives:
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- 1. Explore generalizability on generating word definitions for unseen words
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- 2. Explore the utility of word embeddings by definition models
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  How to run:
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  ```python
 
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  To my knowledge, this is the first public model trained on a word definition task.
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  Similar work: [Zero-shot Word Sense Disambiguation using Sense Definition Embeddings](https://aclanthology.org/P19-1568.pdf)
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+ This model in particular is a on-shot learner for unseen words, as it has to infer the definition by only one example
 
 
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  How to run:
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  ```python