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---
thumbnail: "Аn open multilingual readability scoring model TRank"
base_model: "Peltarion/xlm-roberta-longformer-base-4096"
tags:
- arxiv:2406.01835
- Readability
- Multilingual
- Wikipedia
license: mit
language:
- yi
- xh
- fy
- cy
- vi
- uz
- ug
- ur
- uk
- tr
- th
- te
- ta
- sv
- sw
- su
- es
- so
- sl
- sk
- si
- sd
- sr
- gd
- sa
- ru
- ro
- pa
- pt
- pl
- fa
- ps
- om
- or
- 'no'
- ne
- mn
- mr
- ml
- ms
- mg
- mk
- lt
- lv
- la
- lo
- ky
- ku
- ko
- km
- kk
- kn
- jv
- ja
- it
- ga
- id
- is
- hu
- hi
- he
- ha
- gu
- el
- de
- ka
- gl
- fr
- fi
- tl
- et
- eo
- en
- nl
- da
- cs
- hr
- zh
- ca
- my
- bg
- br
- bs
- bn
- be
- eu
- az
- as
- hy
- ar
- am
- af
- sq
pipeline_tag: text-classification
---
# Open Multilingual Text Readability Scoring Model (TRank)
[](https://doi.org/10.48550/arXiv.2406.01835)
[](https://gitlab.wikimedia.org/repos/research/readability-experiments)
## 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](https://gitlab.wikimedia.org/repos/research/readability-experiments).
## 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},
}
```
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