IteraTeR RoBERTa model

This model was obtained by fine-tuning roberta-large on IteraTeR-human-sent dataset.

Paper: Understanding Iterative Revision from Human-Written Text
Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang

Edit Intention Prediction Task

Given a pair of original sentence and revised sentence, our model can predict the edit intention for this revision pair.
More specifically, the model will predict the probability of the following edit intentions:

Edit Intention	Definition	Example
clarity	Make the text more formal, concise, readable and understandable.	Original: It's like a house which anyone can enter in it. Revised: It's like a house which anyone can enter.
fluency	Fix grammatical errors in the text.	Original: In the same year he became the Fellow of the Royal Society. Revised: In the same year, he became the Fellow of the Royal Society.
coherence	Make the text more cohesive, logically linked and consistent as a whole.	Original: Achievements and awards Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. Revised: Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy.
style	Convey the writer’s writing preferences, including emotions, tone, voice, etc..	Original: She was last seen on 2005-10-22. Revised: She was last seen on October 22, 2005.
meaning-changed	Update or add new information to the text.	Original: This method improves the model accuracy from 64% to 78%. Revised: This method improves the model accuracy from 64% to 83%.

Usage

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("wanyu/IteraTeR-ROBERTA-Intention-Classifier")
model = AutoModelForSequenceClassification.from_pretrained("wanyu/IteraTeR-ROBERTA-Intention-Classifier")

id2label = {0: "clarity", 1: "fluency", 2: "coherence", 3: "style", 4: "meaning-changed"}

before_text = 'I likes coffee.'
after_text = 'I like coffee.'
model_input = tokenizer(before_text, after_text, return_tensors='pt')
model_output = model(**model_input)
softmax_scores = torch.softmax(model_output.logits, dim=-1)
pred_id = torch.argmax(softmax_scores)
pred_label = id2label[pred_id.int()]