metadata
datasets:
- IteraTeR_full_sent
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()]