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---
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datasets:
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- IteraTeR_full_sent
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---
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# IteraTeR RoBERTa model
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This model was obtained by fine-tuning [roberta-large](https://huggingface.co/roberta-large) on [IteraTeR-human-sent](https://huggingface.co/datasets/wanyu/IteraTeR_human_sent) dataset.
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Paper: [Understanding Iterative Revision from Human-Written Text](https://arxiv.org/abs/2203.03802) <br>
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Authors: Wanyu Du, Vipul Raheja, Dhruv Kumar, Zae Myung Kim, Melissa Lopez, Dongyeop Kang
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## Edit Intention Prediction Task
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Given a pair of original sentence and revised sentence, our model can predict the edit intention for this revision pair.<br>
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More specifically, the model will predict the probability of the following edit intentions:
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<table>
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<tr>
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<th>Edit Intention</th>
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<th>Definition</th>
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<th>Example</th>
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</tr>
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<tr>
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<td>clarity</td>
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<td>Make the text more formal, concise, readable and understandable.</td>
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<td>
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Original: It's like a house which anyone can enter in it. <br>
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Revised: It's like a house which anyone can enter.
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</td>
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</tr>
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<tr>
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<td>fluency</td>
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<td>Fix grammatical errors in the text.</td>
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<td>
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Original: In the same year he became the Fellow of the Royal Society. <br>
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Revised: In the same year, he became the Fellow of the Royal Society.
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</td>
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</tr>
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<tr>
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<td>coherence</td>
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<td>Make the text more cohesive, logically linked and consistent as a whole.</td>
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<td>
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Original: Achievements and awards Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy. <br>
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Revised: Among his other activities, he founded the Karachi Film Guild and Pakistan Film and TV Academy.
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</td>
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</tr>
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<tr>
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<td>style</td>
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<td>Convey the writer’s writing preferences, including emotions, tone, voice, etc..</td>
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<td>
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Original: She was last seen on 2005-10-22. <br>
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Revised: She was last seen on October 22, 2005.
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</td>
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</tr>
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<tr>
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<td>meaning-changed</td>
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<td>Update or add new information to the text.</td>
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<td>
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Original: This method improves the model accuracy from 64% to 78%. <br>
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Revised: This method improves the model accuracy from 64% to 83%.
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</td>
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</tr>
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</table>
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## Usage
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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tokenizer = AutoTokenizer.from_pretrained("wanyu/IteraTeR-ROBERTA-Intention-Classifier")
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model = AutoModelForSequenceClassification.from_pretrained("wanyu/IteraTeR-ROBERTA-Intention-Classifier")
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id2label = {0: "clarity", 1: "fluency", 2: "coherence", 3: "style", 4: "meaning-changed"}
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before_text = 'I likes coffee.'
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after_text = 'I like coffee.'
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model_input = tokenizer(before_text, after_text, return_tensors='pt')
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model_output = model(**model_input)
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softmax_scores = torch.softmax(model_output.logits, dim=-1)
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pred_id = torch.argmax(softmax_scores)
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pred_label = id2label[pred_id.int()]
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``` |
