pytorch t5 seq2seq ISO 639-1 code for your language, or `multilingual` dataset:array of dataset identifiers arxiv:1910.10683 transformers array of tags license:any valid license identifier text2text-generation
.gitattributes README.md config.json pytorch_model.bin special_tokens_map.json spiece.model tokenizer_config.json
---
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 " 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 .'


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"
```
