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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 AutoModelForSeq2SeqLM, AutoTokenizer
AutoModelForSeq2SeqLM.from_pretrained("jpelhaw/t5-word-sense-disambiguation")
AutoTokenizer.from_pretrained("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"
```
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