| [Codegen](https://huggingface.co/Salesforce/codegen-16B-mono) is a model for conversational program synthesis, where each problem is interactively solved in multiple steps, each consisting of a natural language specification from the user and a synthesized subprogram from the system. | |
| It was sequentially trained on three datasets: | |
| - [The Pile](https://huggingface.co/datasets/the_pile) | |
| - A 341GB subset of Google’s [BigQuery dataset](https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code) of code files from multiple programming languages, keeping only 6: C, C++, Go, Java, JavaScript, and Python | |
| - 217GB of Python data from GitHub repositories | |
| The second and third datasets used the following preprocessing: | |
| - Exact match deduplication | |
| - Filtering: | |
| - Exact match deduplication | |
| - Average line length < 100 tokens | |
| - Maximum line length < 1000 MB | |
| - Characters being decimal or hexadecimal digits >90% | |