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grammarly
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coedit-xl-composite

Text2Text Generation
Transformers
PyTorch
English
t5
text-generation-inference
Inference Endpoints
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coedit-xl-composite / README.md
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metadata
license: apache-2.0
datasets:
  - asset
  - wi_locness
  - GEM/wiki_auto_asset_turk
  - discofuse
  - zaemyung/IteraTeR_plus
language:
  - en
metrics:
  - sari
  - bleu
  - accuracy

Model Card for CoEdIT-xl-composite

This model was obtained by fine-tuning the corresponding google/flan-t5-xl model on the CoEdIT-Composite dataset. Details of the dataset can be found in our paper and repository.

Paper: CoEdIT: ext Editing by Task-Specific Instruction Tuning Authors: Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang

Model Details

Model Description

  • Language(s) (NLP): English
  • Finetuned from model: google/flan-t5-xl

Model Sources [optional]

How to use

We make available the models presented in our paper.

Model Number of parameters
CoEdIT-large 770M
CoEdIT-xl 3B
CoEdIT-xxl 11B

Uses

Text Revision Task

Given an edit instruction and an original text, our model can generate the edited version of the text.

task_specs

Usage 

from transformers import AutoTokenizer, T5ForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-large")
model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-large")
input_text = 'Fix grammatical errors in this sentence: New kinds of vehicles will be invented with new technology than today.'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=256)
edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)[0]

Software

https://github.com/vipulraheja/coedit

Citation

BibTeX:

[More Information Needed]

APA:

[More Information Needed]