# Architecture

* **Data Collection**: Gather raw data from various sources, including text, images, audio, etc.
* **Data Cleaning**: Remove duplicates, irrelevant or erroneous data, and handle missing or anomalous values.
* **Data Annotation**: Annotate the data as needed to ensure accuracy and consistency.

## Initial Data Filtering

* **Data Preprocessing**: Perform preprocessing steps such as normalization, standardization, or tokenization.
* **Data Classification**: Classify the data based on features and labels to perform initial filtering and identify potentially useful datasets.

## Data Evaluation Layer

* **Define Evaluation Metrics**: Establish metrics for assessing data quality, such as accuracy, completeness, consistency, and relevance.
* **Develop Evaluation Models**: Create or select appropriate evaluation models, which may include rule-based systems, statistical methods, or machine learning models.
  * **Rule-Based Evaluation**: Set rules and standards to assess data quality.
  * **Statistical Methods**: Use statistical methods to detect anomalies and biases in the data.
  * **Machine Learning Models**: Train machine learning models to evaluate data quality.
* **Execute Evaluation**: Apply the evaluation models to the data, generating quality reports and scores.
* **Feedback Loop**: Adjust data collection and processing workflows based on evaluation results to improve data quality.

##  Data Selection and Filtering

* **Filter Based on Evaluation Results**: Select high-quality datasets based on evaluation outcomes.
* **Data Integration**: Integrate the filtered datasets into the training dataset, ensuring consistency and high quality.

<figure><img src="/files/ecJ89t0GlkVjsyipm6Nu" alt=""><figcaption></figcaption></figure>


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