Create README.md

README.md

@@ -0,0 +1,26 @@
+# T5-define  
+
+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]'
+
+To my knowledge, this is the first public model trained on a word definition task.
+Similar work: [Zero-shot Word Sense Disambiguation using Sense Definition Embeddings](https://aclanthology.org/P19-1568.pdf)
+
+For this project, there are two objectives:
+1. Explore generalizability on generating word definitions for unseen words
+2. Explore the utility of word embeddings by definition models
+
+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]
+tokenizer.decode(generated_tokens)
+```