🗣️Collect and Preprocess the Unsupervised Corpus of Tokens from Various Oracles

Data Collection from Oracles

Identifying Oracles

The first step in collecting the unsupervised corpus of tokens is identifying suitable oracles. Oracles are decentralized sources of data that can provide a diverse and comprehensive collection of text data. These can include:

• Web Scraping: Automated scripts that collect data from publicly available websites, forums, blogs, and news sites.

• Public Datasets: Utilizing large, publicly available datasets such as Common Crawl, Wikipedia dumps, and other open-source text corpora.

• Community Contributions: Encouraging the community to contribute text data from various sources, ensuring a wide range of topics and writing styles.

• Partnerships with Data Providers: Collaborating with organizations that can provide access to proprietary or specialized text datasets.

Ensuring Data Diversity

To train a robust language model, it is essential to gather data that covers a broad spectrum of topics, languages, and writing styles. This diversity helps the model generalize better and perform well across different tasks.

Data Preprocessing

Text Cleaning

The raw text data collected from oracles often contains noise and irrelevant content. Text cleaning involves several steps:

• Removing HTML Tags: Stripping away HTML tags from web-scraped content.

• Normalizing Text: Converting all text to lowercase, removing special characters, and normalizing unicode characters.

• Removing Stop Words: Eliminating common words that do not contribute much to the meaning, such as "and," "the," and "is," if appropriate for the model's objectives.

• Tokenization: Splitting the text into individual tokens (words or subwords) that can be processed by the model.

Handling Punctuation and Special Characters

Properly handling punctuation and special characters is crucial for maintaining the integrity of the text data. This step includes:

• Retaining Important Punctuation: Keeping punctuation marks that contribute to the meaning of sentences, such as periods, commas, and question marks.

• Removing Noise: Discarding non-informative special characters, such as random symbols or excessive punctuation marks.

De-duplication

To prevent the model from learning repetitive patterns, it is important to remove duplicate sentences or paragraphs from the corpus. This ensures that the dataset remains diverse and informative.

Sentence Segmentation

Dividing the text into coherent sentences or segments helps in constructing meaningful context windows for training the model. This involves:

• Sentence Detection: Identifying sentence boundaries using punctuation marks and capitalization patterns.

• Context Window Construction: Creating context windows of a fixed size kkk, which will be used to predict the next token in the sequence.

Language Detection and Filtering

If the collected data includes multiple languages, it might be necessary to detect and filter the text based on the desired language(s) for training. This step ensures that the model focuses on the relevant linguistic patterns.

Data Augmentation

To further enhance the training data, various data augmentation techniques can be applied:

• Synonym Replacement: Replacing words with their synonyms to create variations of sentences.

• Back-Translation: Translating text to another language and then back to the original language to generate paraphrases.

• Random Insertion and Deletion: Randomly inserting or deleting words to increase data variability.

Balancing the Dataset

Ensuring that the dataset is balanced in terms of different topics, styles, and languages (if applicable) prevents the model from becoming biased towards any particular type of content.

Storing and Managing the Preprocessed Data

After preprocessing, the cleaned and segmented text data is stored in a structured format that is easily accessible for training. This involves:

• Data Batching: Organizing the text into batches for efficient loading during the training process.

• Version Control: Maintaining versions of the dataset to track changes and improvements over time.

• Data Security and Privacy: Implementing measures to protect the data from unauthorized access and ensuring compliance with relevant data protection regulations.

By thoroughly collecting and preprocessing the unsupervised corpus of tokens from various oracles, we ensure that the training data is of high quality, diverse, and well-suited for developing a robust and effective DGPT model.