trokhymovych/TRank_readability

Open Multilingual Text Readability Scoring Model (TRank)

Overview

This repository contains an open multilingual readability scoring model TRank, presented in the ACL'24 paper An Open Multilingual System for Scoring Readability of Wikipedia. The model is designed to evaluate the readability of text across multiple languages.

Features

• Multilingual Support: Evaluates readability in multiple languages.
• Pairwise Ranking: Trained using a Siamese architecture with Margin Ranking Loss to differentiate and rank texts from hardest to simplest.
• Long Context Window: Utilizes the Longformer architecture of the base model, supporting inputs up to 4096 tokens.

Model Training

The model training implementation can be found in the Readability Experiments repo.

Usage example

import torch
import torch.nn as nn
from transformers import AutoModel
from huggingface_hub import PyTorchModelHubMixin
from transformers import AutoTokenizer

# Define the model:
BASE_MODEL = "Peltarion/xlm-roberta-longformer-base-4096"
class ReadabilityModel(nn.Module, PyTorchModelHubMixin):
    def __init__(self, model_name=BASE_MODEL):
        super(ReadabilityModel, self).__init__()
        self.model = AutoModel.from_pretrained(model_name)
        self.drop = nn.Dropout(p=0.2)
        self.fc = nn.Linear(768, 1)

    def forward(self, ids, mask):
        out = self.model(input_ids=ids, attention_mask=mask,
                         output_hidden_states=False)
        out = self.drop(out[1])
        outputs = self.fc(out)

        return outputs
    
# Load the model:
model = ReadabilityModel.from_pretrained("trokhymovych/TRank_readability")

# Load the tokenizer:
tokenizer = AutoTokenizer.from_pretrained("trokhymovych/TRank_readability")

# Set the model to evaluation mode
model.eval()
# Example input text
input_text = "This is an example sentence to evaluate readability."
# Tokenize the input text
inputs = tokenizer.encode_plus(
    input_text,
    add_special_tokens=True,
    max_length=512,
    truncation=True,
    padding='max_length',
    return_tensors='pt'
)
ids = inputs['input_ids']
mask = inputs['attention_mask']

# Make prediction
with torch.no_grad():
    outputs = model(ids, mask)
    readability_score = outputs.item()
    
# Print the input text and the readability score
print(f"Input Text: {input_text}")
print(f"Readability Score: {readability_score}")

Citation

Preprint:

@misc{trokhymovych2024openmultilingualscoringreadability,
      title={An Open Multilingual System for Scoring Readability of Wikipedia}, 
      author={Mykola Trokhymovych and Indira Sen and Martin Gerlach},
      year={2024},
      eprint={2406.01835},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.01835}, 
}