|
--- |
|
language: en |
|
thumbnail: url to a thumbnail used in social sharing |
|
tags: |
|
- array |
|
- of |
|
- tags |
|
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 |
|
|
|
If you are using this model in your research work, please cite |
|
|
|
```bib |
|
@article{wahle2021incorporating, |
|
title={Incorporating Word Sense Disambiguation in Neural Language Models}, |
|
author={Wahle, Jan Philip and Ruas, Terry and Meuschke, Norman and Gipp, Bela}, |
|
journal={arXiv preprint arXiv:2106.07967}, |
|
year={2021} |
|
} |
|
``` |
|
|
|
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 |
|
|
|
model = AutoModelForSeq2SeqLM.from_pretrained("jpelhaw/t5-word-sense-disambiguation") |
|
tokenizer = 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 language ", 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 drink consisting of an infusion of ground coffee beans" |
|
``` |
|
|
