EuroBERT-210m-Quality-NL Open-source Model - Free Evaluation of Natural Language and Programming Text Quality

Eurobert 210m Quality NL

Developed by TempestTeam

Automatically assesses text data quality for both natural and programming languages, offering both unified and dual-model solutions.

Text Classification

Transformers

Supports Multiple LanguagesOpen Source License:Apache-2.0 #Multilingual Quality Assessment #Code Quality Detection #Harmful Content Identification

Downloads 18

Release Time : 3/18/2025

Model Overview

This model employs a clear and intuitive scoring system to automatically evaluate text data quality for natural languages (NL) and programming languages (CL), supporting multiple languages and programming languages.

Model Features

Multilingual Support

Supports natural languages like French, English, Spanish, and programming languages such as Python, Java, JavaScript, C/C++.

Dual-Model Solution

Offers both unified and independent models to handle natural and programming languages separately, catering to different scenario needs.

High-Quality Assessment

Uses a four-tier classification system (harmful, poor, medium, high-quality) to accurately identify text quality.

Model Capabilities

Natural Language Text Quality Assessment

Programming Language Text Quality Assessment

Harmful Content Identification

Multilingual Support

Use Cases

NLP Pipeline

Automatic Text Corpus Validation

Automatically validates text corpus quality in NLP or code generation pipelines.

Improves input data quality for models

Community Content Management

Forum Content Evaluation

Automatically assesses content quality in forums, Stack Overflow, or GitHub communities.

Enhances overall community content quality

System Preprocessing

NLP System Preprocessing

Automated preprocessing to enhance NLP or code generation system performance.

Optimizes system performance

🚀 Automatic Evaluation Models for Textual Data Quality (NL & CL)

Automatically assess the quality of textual data using a clear and intuitive scale, suitable for both natural language (NL) and code language (CL).

🚀 Quick Start

This project offers two different approaches to automatically evaluate the quality of textual data:

A unified model that handles both NL and CL jointly: EuroBERT - 210m - Quality
A dual - model approach that treats NL and CL separately:
- EuroBERT - 210m - Quality - NL for natural language
- EuroBERT - 210m - Quality - CL for code language.

✨ Features

Classification Categories

Harmful: Harmful data, potentially incorrect or dangerous.
Low: Low - quality data with major issues.
Medium: Medium quality, improvable but acceptable.
High: Good to very good quality data, ready for use without reservation.

Supported Languages

Natural Language: French 🇫🇷, English 🇬🇧, Spanish 🇪🇸
Code Language: Python 🐍, Java ☕, JavaScript 📜, C/C++ ⚙️

📚 Documentation

Performance

f1 - score: Unified Model (NL + CL)

Category	Global (NL + CL)	NL	CL
Harmful	0.86	0.93	0.79
Low	0.62	0.81	0.40
Medium	0.63	0.78	0.50
High	0.77	0.81	0.74
Accuracy	0.73	0.83	0.62

f1 - score: Separate Models

Category	Global (NL + CL)	NL	CL
Harmful	0.83	0.93	0.72
Low	0.64	0.76	0.53
Medium	0.63	0.76	0.52
High	0.79	0.81	0.76
Accuracy	0.73	0.82	0.63

Key Performance Metrics

Unified Model (NL + CL):
- Overall accuracy: ~73%
- High reliability on harmful data (f1 - score: 0.86)
Separate Models:
- Natural Language (NL): ~82% accuracy
  - Excellent performance on harmful data (f1 - score: 0.93)
- Code Language (CL): ~63% accuracy
  - Good detection of harmful data (f1 - score: 0.72)

Training Dataset

Public dataset available: TempestTeam/dataset - quality

Common Use Cases

Automatic validation of text corpora before integration into NLP or code generation pipelines.
Quality assessment of community contributions (forums, Stack Overflow, GitHub).
Automated pre - processing to enhance NLP or code generation system performance.

Recommendations

⚠️ Important Note

For specialized contexts, use the separate NL and CL models for optimal results.

💡 Usage Tip

The unified model is suitable for quick assessments when the data context is unknown or mixed.

Citation

Please cite or link back to this model on Hugging Face Hub if used in your projects.

📄 License

This project is licensed under the apache - 2.0 license.

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