
# 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"
```

{"language": "en", "tags": ["array", "of", "tags"], "thumbnail": "url to a thumbnail used in social sharing", "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 ."}]}
transformers pytorch safetensors t5 text2text-generation array of tags en arxiv:1910.10683 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #array #of #tags #en #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# T5-large for Word Sense Disambiguation

If you are using this model in your research work, please cite



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:



# T5-large for Word Sense Disambiguation

If you are using this model in your research work, please cite



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:
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #array #of #tags #en #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # T5-large for Word Sense Disambiguation

If you are using this model in your research work, please cite



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:
passage: TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #array #of #tags #en #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# T5-large for Word Sense Disambiguation

If you are using this model in your research work, please cite



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: