pytorch longformer text-classification ISO 639-1 code for your language, or `multilingual` dataset:array of dataset identifiers arxiv:2004.05150 transformers array of tags license:any valid license identifier
.gitattributes README.md config.json merges.txt pytorch_model.bin special_tokens_map.json tokenizer_config.json vocab.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: "Plagiarism is the representation of another author's writing, thoughts, ideas, or expressions as one's own work."
---

# T5-large for Word Sense Disambiguation

This is the checkpoint for T5-large after being trained on the Machine-Paraphrased Plagiarism Dataset: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3608000.svg)](https://doi.org/10.5281/zenodo.3608000)

Additional information about this model:

* [The longformer-base-4096 model page](https://huggingface.co/allenai/longformer-base-4096)
* [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf)
* [Official implementation by AllenAI](https://github.com/allenai/longformer)

The model can be loaded to perform Plagiarism like so:

```py
from transformers import AutoModelForSequenceClassification, AutoTokenizer

AutoModelForSequenceClassification("jpelhaw/longformer-base-plagiarism-detection")
AutoTokenizer.from_pretrained("jpelhaw/longformer-base-plagiarism-detection")

input = 'Plagiarism is the representation of another author's writing, thoughts, ideas, or expressions as one's own work.'


example = tokenizer.tokenize(input, add_special_tokens=True)

answer = model(**example)
                                
# "plagiarised"
```