I would like to create a language model similar to OpenAI's ChatGPT that can be truly open source. What would be a good way to generate training data for this task?
Generating training data for a language model is a crucial step in creating a successful language model. There are several ways to generate training data for a language model that can be truly open source. Here are some suggestions:

1. Web Scraping: You can scrape text data from the internet, such as articles, blogs, and other web pages. This can provide you with a large amount of text data that you can use to train your language model.
2. Open Source Text Datasets: There are many open-source text datasets available that you can use to train your language model, such as the Gutenberg Project, Common Crawl, and Open Subtitles.
3. User-generated content: You can use user-generated content such as social media posts, forum threads, and comments to generate training data. This can be a good way to create a dataset that reflects modern language usage.
4. Collaborative efforts: You can collaborate with other developers to pool together text data from various sources. This can be an effective way to generate a large and diverse dataset.

Once you have a dataset, you can use it to train your language model using various techniques such as unsupervised learning, supervised learning, or a combination of both. Remember that the quality of your training data is critical to the performance of your language model. Therefore, it is essential to ensure that the data is diverse, representative, and free of bias.
how in case of web scraping should be made filtering and validation of data so training will not be spoiled with misleading information?
In web scraping, it's important to filter and validate data to ensure that the training data is not spoiled with misleading or irrelevant information. Here are some ways to do this:

1. Use filters: Use filters to remove irrelevant or unwanted data. For example, you can filter out advertisements or images from the scraped data. You can also use filters to remove data that doesn't match a certain pattern or format.
2. Check for duplicates: Check for duplicate data in the scraped data set and remove them. Duplicate data can distort the training data and make it less accurate.
3. Check for outliers: Check for data that falls outside the normal range and remove it. Outliers can also distort the training data and make it less accurate.
4. Validate data: Validate the scraped data to ensure that it is accurate and reliable. You can do this by comparing the scraped data with other sources or by manually verifying the data.
5. Use machine learning algorithms: Use machine learning algorithms to filter and validate the data automatically. For example, you can use clustering algorithms to group similar data points and remove outliers.

By filtering and validating the data in these ways, you can ensure that the training data is accurate and reliable, which will improve the performance of the machine learning model.
Is there any way to contextualize that something should be considered fiction or nonfiction?