--- 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" ```