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--- |
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language: "ISO 639-1 code for your language, or `multilingual`" |
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thumbnail: "url to a thumbnail used in social sharing" |
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tags: |
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- array |
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- of |
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- tags |
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license: "any valid license identifier" |
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datasets: |
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- array of dataset identifiers |
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metrics: |
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- array of metric identifiers |
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widget: |
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- 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 ." |
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--- |
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# T5-large for Word Sense Disambiguation |
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This is the checkpoint for T5-large after being trained on the [SemCor 3.0 dataset](http://lcl.uniroma1.it/wsdeval/). |
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Additional information about this model: |
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* [The t5-large model page](https://huggingface.co/t5-large) |
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* [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) |
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* [Official implementation by Google](https://github.com/google-research/text-to-text-transfer-transformer) |
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The model can be loaded to perform a few-shot classification like so: |
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```py |
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from transformers import AutoModelForConditionalGeneration, AutoTokenizer |
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AutoModelForConditionalGeneration.from_pretrained("jpelhaw/t5-word-sense-disambiguation") |
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AutoTokenizer("jpelhaw/t5-word-sense-disambiguation") |
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input = 'question: which description describes the word " peculiarities " best in the following context? \ |
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descriptions: [ " an odd or unusual characteristic " , " a distinguishing trait " , or " something unusual -- perhaps worthy of collecting " ] \ |
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context: The art of change-ringing is peculiar to the English , and , like most English \' peculiarities \' , unintelligible to the rest of the world .' |
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example = tokenizer.tokenize(input, add_special_tokens=True) |
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answer = model.generate(input_ids=example['input_ids'], |
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attention_mask=example['attention_mask'], |
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max_length=135) |
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# "a distinguishing trait" |
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``` |
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