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README.md

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+---
+language: "ISO 639-1 code for your language, or `multilingual`"
+thumbnail: "url to a thumbnail used in social sharing"
+tags:
+- array
+- of
+- tags
+license: "any valid license identifier"
+datasets:
+- array of dataset identifiers
+metrics:
+- array of metric identifiers
+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
+
+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 AutoModelForConditionalGeneration, AutoTokenizer
+
+AutoModelForConditionalGeneration.from_pretrained("jpelhaw/t5-word-sense-disambiguation")
+AutoTokenizer("jpelhaw/t5-word-sense-disambiguation")
+
+input = 'question: which description describes the word " peculiarities " best in the following context? \
+  descriptions: [  " an odd or unusual characteristic " ,  " a distinguishing trait " ,  or " something unusual -- perhaps worthy of collecting " ] \
+  context: The art of change-ringing is peculiar to the English , and , like most English \' peculiarities \'  , unintelligible to the rest of the world .'
+
+
+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 distinguishing trait"
+```