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Dingo: A Comprehensive Data Quality Evaluation Tool

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Dingo is a data quality evaluation tool that automatically detects data quality issues in datasets. It provides built-in rules and model evaluation methods, supports text and multimodal datasets, and offers local CLI and SDK usage. Dingo is designed for easy integration into evaluation platforms like OpenCompass.

README:

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Changelog

2024/12/27: Project Initialization

Introduction

Dingo is a data quality evaluation tool that helps you automatically detect data quality issues in your datasets. Dingo provides a variety of built-in rules and model evaluation methods, and also supports custom evaluation methods. Dingo supports commonly used text datasets and multimodal datasets, including pre-training datasets, fine-tuning datasets, and evaluation datasets. In addition, Dingo supports multiple usage methods, including local CLI and SDK, making it easy to integrate into various evaluation platforms, such as OpenCompass.

Architecture Diagram

Quick Start

Installation

pip install dingo-python

Example Use Cases

1. Evaluate Local Text File (Plaintext)

from dingo.io import InputArgs
from dingo.exec import Executor

# Evaluate a plaintext file
input_data = {
    "eval_group": "sft",          # Rule set for SFT data
    "input_path": "data.txt",      # Path to local text file
    "dataset": "local",
    "data_format": "plaintext",    # Format: plaintext
    "save_data": True              # Save evaluation results
}

input_args = InputArgs(**input_data)
executor = Executor.exec_map["local"](input_args)
result = executor.execute()
print(result)

2. Evaluate Hugging Face Dataset

from dingo.io import InputArgs
from dingo.exec import Executor

# Evaluate a dataset from Hugging Face
input_data = {
    "eval_group": "sft",           # Rule set for SFT data
    "input_path": "tatsu-lab/alpaca", # Dataset from Hugging Face
    "data_format": "plaintext",    # Format: plaintext
    "save_data": True              # Save evaluation results
}

input_args = InputArgs(**input_data)
executor = Executor.exec_map["local"](input_args)
result = executor.execute()
print(result)

3. Evaluate JSON/JSONL Format

from dingo.io import InputArgs
from dingo.exec import Executor

# Evaluate a JSON file
input_data = {
    "eval_group": "default",       # Default rule set
    "input_path": "data.json",     # Path to local JSON file
    "dataset": "local",
    "data_format": "json",         # Format: json
    "column_content": "text",      # Column containing the text to evaluate
    "save_data": True              # Save evaluation results
}

input_args = InputArgs(**input_data)
executor = Executor.exec_map["local"](input_args)
result = executor.execute()
print(result)

4. Using LLM for Evaluation

from dingo.io import InputArgs
from dingo.exec import Executor

# Evaluate using GPT model
input_data = {
    "input_path": "data.jsonl",    # Path to local JSONL file
    "dataset": "local",
    "data_format": "jsonl",
    "column_content": "content",
    "custom_config": {
        "prompt_list": ["PromptRepeat"],  # Prompt to use
        "llm_config": {
            "detect_text_quality": {
                "model": "gpt-4o",
                "key": "YOUR_API_KEY",
                "api_url": "https://api.openai.com/v1/chat/completions"
            }
        }
    }
}

input_args = InputArgs(**input_data)
executor = Executor.exec_map["local"](input_args)
result = executor.execute()
print(result)

Command Line Interface

Evaluate with Rule Sets

python -m dingo.run.cli --input_path data.txt --dataset local -e sft --data_format plaintext --save_data True

Evaluate with LLM (e.g., GPT-4o)

python -m dingo.run.cli --input_path data.json --dataset local -e openai --data_format json --column_content text --custom_config config_gpt.json --save_data True

Example config_gpt.json:

{
  "llm_config": {
    "openai": {
      "model": "gpt-4o",
      "key": "YOUR_API_KEY",
      "api_url": "https://api.openai.com/v1/chat/completions"
    }
  }
}

GUI Visualization

After evaluation (with save_data=True), a frontend page will be automatically generated. To manually start the frontend:

python -m dingo.run.vsl --input output_directory

Where output_directory contains the evaluation results with a summary.json file.

Online Demo

Try Dingo on our online demo: (Hugging Face)🤗

Data Quality Metrics

Dingo classifies data quality issues into 7 dimensions of Quality Metrics. Each dimension can be evaluated using both rule-based methods and LLM-based prompts:

Quality Metric	Description	Rule Examples	LLM Prompt Examples
COMPLETENESS	Checks if data is incomplete or missing	`RuleColonEnd`, `RuleContentNull`	Evaluates if text abruptly ends with a colon or ellipsis, has mismatched parentheses, or missing critical components
EFFECTIVENESS	Checks if data is meaningful and properly formatted	`RuleAbnormalChar`, `RuleHtmlEntity`, `RuleSpecialCharacter`	Detects garbled text, words stuck together without spaces, and text lacking proper punctuation
FLUENCY	Checks if text is grammatically correct and reads naturally	`RuleAbnormalNumber`, `RuleNoPunc`, `RuleWordStuck`	Identifies excessively long words, text fragments without punctuation, or content with chaotic reading order
RELEVANCE	Detects irrelevant content within the data	`RuleHeadWord` variants for different languages	Examines for irrelevant information like citation details, headers/footers, entity markers, HTML tags
SECURITY	Identifies sensitive information or value conflicts	`RuleIDCard`, `RuleUnsafeWords`	Checks for personal information, and content related to gambling, pornography, political issues
SIMILARITY	Detects repetitive or highly similar content	`RuleDocRepeat`	Evaluates text for consecutive repeated content or multiple occurrences of special characters
UNDERSTANDABILITY	Assesses how easily data can be interpreted	`RuleCapitalWords`	Ensures LaTeX formulas and Markdown are correctly formatted, with proper segmentation and line breaks

LLM Quality Assessment

Dingo provides several LLM-based assessment methods defined by prompts in the dingo/model/prompt directory. These prompts are registered using the prompt_register decorator and can be combined with LLM models for quality evaluation:

Text Quality Assessment Prompts

Prompt Type	Metric	Description
`TEXT_QUALITY_V2`, `TEXT_QUALITY_V3`	Various quality dimensions	Comprehensive text quality evaluation covering effectiveness, relevance, completeness, understandability, similarity, fluency, and security
`QUALITY_BAD_EFFECTIVENESS`	Effectiveness	Detects garbled text and anti-crawling content
`QUALITY_BAD_SIMILARITY`	Similarity	Identifies text repetition issues
`WORD_STICK`	Fluency	Checks for words stuck together without proper spacing
`CODE_LIST_ISSUE`	Completeness	Evaluates code blocks and list formatting issues
`UNREAD_ISSUE`	Effectiveness	Detects unreadable characters due to encoding issues

3H Assessment Prompts (Honest, Helpful, Harmless)

Prompt Type	Metric	Description
`QUALITY_HONEST`	Honesty	Evaluates if responses provide accurate information without fabrication or deception
`QUALITY_HELPFUL`	Helpfulness	Assesses if responses address questions directly and follow instructions appropriately
`QUALITY_HARMLESS`	Harmlessness	Checks if responses avoid harmful content, discriminatory language, and dangerous assistance

Domain-Specific Assessment Prompts

Prompt Type	Metric	Description
`TEXT_QUALITY_KAOTI`	Exam question quality	Specialized assessment for evaluating the quality of exam questions, focusing on formula rendering, table formatting, paragraph structure, and answer formatting
`Html_Abstract`	HTML extraction quality	Compares different methods of extracting Markdown from HTML, evaluating completeness, formatting accuracy, and semantic coherence

Classification Prompts

Prompt Type	Metric	Description
`CLASSIFY_TOPIC`	Topic Categorization	Classifies text into categories like language processing, writing, code, mathematics, role-play, or knowledge Q&A
`CLASSIFY_QR`	Image Classification	Identifies images as CAPTCHA, QR code, or normal images

Image Assessment Prompts

Prompt Type	Metric	Description
`IMAGE_RELEVANCE`	Image Relevance	Evaluates if an image matches reference image in terms of face count, feature details, and visual elements

Using LLM Assessment in Evaluation

To use these assessment prompts in your evaluations, specify them in your configuration:

input_data = {
    # Other parameters...
    "custom_config": {
        "prompt_list": ["QUALITY_BAD_SIMILARITY"],  # Specific prompt to use
        "llm_config": {
            "detect_text_quality": {  # LLM model to use
                "model": "gpt-4o",
                "key": "YOUR_API_KEY",
                "api_url": "https://api.openai.com/v1/chat/completions"
            }
        }
    }
}

You can customize these prompts to focus on specific quality dimensions or to adapt to particular domain requirements. When combined with appropriate LLM models, these prompts enable comprehensive evaluation of data quality across multiple dimensions.

Rule Groups

Dingo provides pre-configured rule groups for different types of datasets:

Group	Use Case	Example Rules
`default`	General text quality	`RuleColonEnd`, `RuleContentNull`, `RuleDocRepeat`, etc.
`sft`	Fine-tuning datasets	Rules from `default` plus `RuleLineStartWithBulletpoint`
`pretrain`	Pre-training datasets	Comprehensive set of 20+ rules including `RuleAlphaWords`, `RuleCapitalWords`, etc.

To use a specific rule group:

input_data = {
    "eval_group": "sft",  # Use "default", "sft", or "pretrain"
    # other parameters...
}

Feature Highlights

Multi-source & Multi-modal Support

Data Sources: Local files, Hugging Face datasets, S3 storage
Data Types: Pre-training, fine-tuning, and evaluation datasets
Data Modalities: Text and image

Rule-based & Model-based Evaluation

Built-in Rules: 20+ general heuristic evaluation rules
LLM Integration: OpenAI, Kimi, and local models (e.g., Llama3)
Custom Rules: Easily extend with your own rules and models
Security Evaluation: Perspective API integration

Flexible Usage

Interfaces: CLI and SDK options
Integration: Easy integration with other platforms
Execution Engines: Local and Spark

Comprehensive Reporting

Quality Metrics: 7-dimensional quality assessment
Traceability: Detailed reports for anomaly tracking

User Guide

Custom Rules, Prompts, and Models

If the built-in rules don't meet your requirements, you can create custom ones:

Custom Rule Example

from dingo.model import Model
from dingo.model.rule.base import BaseRule
from dingo.config.config import DynamicRuleConfig
from dingo.io import MetaData
from dingo.model.modelres import ModelRes

@Model.rule_register('QUALITY_BAD_RELEVANCE', ['default'])
class MyCustomRule(BaseRule):
    """Check for custom pattern in text"""

    dynamic_config = DynamicRuleConfig(pattern=r'your_pattern_here')

    @classmethod
    def eval(cls, input_data: MetaData) -> ModelRes:
        res = ModelRes()
        # Your rule implementation here
        return res

Custom LLM Integration

from dingo.model import Model
from dingo.model.llm.base_openai import BaseOpenAI

@Model.llm_register('my_custom_model')
class MyCustomModel(BaseOpenAI):
    # Custom implementation here
    pass

See more examples in:

Execution Engines

Local Execution

from dingo.io import InputArgs
from dingo.exec import Executor

input_args = InputArgs(**input_data)
executor = Executor.exec_map["local"](input_args)
result = executor.execute()

# Get results
summary = executor.get_summary()        # Overall evaluation summary
bad_data = executor.get_bad_info_list() # List of problematic data
good_data = executor.get_good_info_list() # List of high-quality data

Spark Execution

from dingo.io import InputArgs
from dingo.exec import Executor
from pyspark.sql import SparkSession

# Initialize Spark
spark = SparkSession.builder.appName("Dingo").getOrCreate()
spark_rdd = spark.sparkContext.parallelize([...])  # Your data as MetaData objects

input_args = InputArgs(eval_group="default", save_data=True)
executor = Executor.exec_map["spark"](input_args, spark_session=spark, spark_rdd=spark_rdd)
result = executor.execute()

Evaluation Reports

After evaluation, Dingo generates:

Summary Report (summary.json): Overall metrics and scores
Detailed Reports: Specific issues for each rule violation

Example summary:

{
    "task_id": "d6c922ec-981c-11ef-b723-7c10c9512fac",
    "task_name": "dingo",
    "eval_group": "default",
    "input_path": "test/data/test_local_jsonl.jsonl",
    "output_path": "outputs/d6c921ac-981c-11ef-b723-7c10c9512fac",
    "create_time": "20241101_144510",
    "score": 50.0,
    "num_good": 1,
    "num_bad": 1,
    "total": 2,
    "type_ratio": {
        "QUALITY_BAD_COMPLETENESS": 0.5,
        "QUALITY_BAD_RELEVANCE": 0.5
    },
    "name_ratio": {
        "QUALITY_BAD_COMPLETENESS-RuleColonEnd": 0.5,
        "QUALITY_BAD_RELEVANCE-RuleSpecialCharacter": 0.5
    }
}

Future Plans

[ ] Richer graphic and text evaluation indicators
[ ] Audio and video data modality evaluation
[ ] Small model evaluation (fasttext, Qurating)
[ ] Data diversity evaluation

Limitations

The current built-in detection rules and model methods focus on common data quality problems. For specialized evaluation needs, we recommend customizing detection rules.

Acknowledgments

Contribution

We appreciate all the contributors for their efforts to improve and enhance Dingo. Please refer to the Contribution Guide for guidance on contributing to the project.

License

This project uses the Apache 2.0 Open Source License.

Citation

If you find this project useful, please consider citing our tool:

@misc{dingo,
  title={Dingo: A Comprehensive Data Quality Evaluation Tool for Large Models},
  author={Dingo Contributors},
  howpublished={\url{https://github.com/DataEval/dingo}},
  year={2024}
}

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