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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](https://github.com/open-compass/opencompass). ## Architecture Diagram ![Architecture of dingo](./docs/assets/architeture.png) # Quick Start ## Installation ```shell pip install dingo-python ``` ## Example Use Cases ### 1. Using Evaluate Core ```python from dingo.config.config import DynamicLLMConfig from dingo.io.input.MetaData import MetaData from dingo.model.llm.llm_text_quality_model_base import LLMTextQualityModelBase from dingo.model.rule.rule_common import RuleEnterAndSpace def llm(): data = MetaData( data_id='123', prompt="hello, introduce the world", content="Hello! The world is a vast and diverse place, full of wonders, cultures, and incredible natural beauty." ) LLMTextQualityModelBase.dynamic_config = DynamicLLMConfig( key='', api_url='', # model='', ) res = LLMTextQualityModelBase.eval(data) print(res) def rule(): data = MetaData( data_id='123', prompt="hello, introduce the world", content="Hello! The world is a vast and diverse place, full of wonders, cultures, and incredible natural beauty." ) res = RuleEnterAndSpace().eval(data) print(res) ``` ### 2. Evaluate Local Text File (Plaintext) ```python 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) ``` ### 3. Evaluate Hugging Face Dataset ```python 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) ``` ### 4. Evaluate JSON/JSONL Format ```python 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) ``` ### 5. Using LLM for Evaluation ```python 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 ```shell 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) ```shell 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`: ```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: ```shell python -m dingo.run.vsl --input output_directory ``` Where `output_directory` contains the evaluation results with a `summary.json` file. ![GUI output](docs/assets/dingo_gui.png) ## Online Demo Try Dingo on our online demo: [(Hugging Face)🤗](https://huggingface.co/spaces/DataEval/dingo) # 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 | | `DATAMAN_ASSESSMENT` | Data Quality & Domain | Evaluates pre-training data quality using the DataMan methodology (14 standards, 15 domains). Assigns a score (0/1), domain type, quality status, and reason. | ### 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: ```python 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: ```python 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 ```python 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 ```python 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: - [Register Rules](examples/register/sdk_register_rule.py) - [Register Prompts](examples/register/sdk_register_prompt.py) - [Register Models](examples/register/sdk_register_llm.py) ## Execution Engines ### Local Execution ```python 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 ```python 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: 1. **Summary Report** (`summary.json`): Overall metrics and scores 2. **Detailed Reports**: Specific issues for each rule violation Example summary: ```json { "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 } } ``` # MCP Server (Experimental) Dingo includes an experimental Model Context Protocol (MCP) server. For details on running the server and integrating it with clients like Cursor, please see the dedicated documentation: [**Dingo MCP Server Documentation (README_mcp.md)**](README_mcp.md) # Research & Publications - **"Comprehensive Data Quality Assessment for Multilingual WebData"** : [WanJuanSiLu: A High-Quality Open-Source Webtext Dataset for Low-Resource Languages](https://arxiv.org/pdf/2501.14506) - **"Pre-training data quality using the DataMan methodology"** : [DataMan: Data Manager for Pre-training Large Language Models](https://openreview.net/pdf?id=eNbA8Fqir4) # 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 - [RedPajama-Data](https://github.com/togethercomputer/RedPajama-Data) - [mlflow](https://github.com/mlflow/mlflow) # Contribution We appreciate all the contributors for their efforts to improve and enhance `Dingo`. Please refer to the [Contribution Guide](docs/en/CONTRIBUTING.md) for guidance on contributing to the project. # License This project uses the [Apache 2.0 Open Source License](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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Dingo