metadata
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
@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.
Additional information about this model:
- The t5-large model page
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- Official implementation by Google
The model can be loaded to perform a few-shot classification like so:
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"