metadata
language: ISO 639-1 code for your language, or `multilingual`
thumbnail: url to a thumbnail used in social sharing
tags:
- array
- of
- tags
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.
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 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 drink consisting of an infusion of ground coffee beans"