| import streamlit as st | |
| from defaults import ( | |
| PROJECT_NAME, | |
| ARGILLA_SPACE_REPO_ID, | |
| DATASET_REPO_ID, | |
| ARGILLA_URL, | |
| PROJECT_SPACE_REPO_ID, | |
| DIBT_PARENT_APP_URL, | |
| ) | |
| from utils import project_sidebar | |
| st.set_page_config("Domain Data Grower", page_icon="🧑🌾") | |
| project_sidebar() | |
| if PROJECT_NAME == "DEFAULT_DOMAIN": | |
| st.warning( | |
| "Please set up the project configuration in the parent app before proceeding." | |
| ) | |
| st.stop() | |
| st.header("🧑🌾 Domain Data Grower") | |
| st.divider() | |
| st.markdown( | |
| """ | |
| ## 🌱 Create a dataset seed for aligning models to a specific domain | |
| This app helps you create a dataset seed for building diverse domain-specific datasets for aligning models. | |
| Alignment datasets are used to fine-tune models to a specific domain or task, but as yet, there's a shortage of diverse datasets for this purpose. | |
| """ | |
| ) | |
| st.markdown( | |
| """ | |
| ## 🚜 How it works | |
| You can create a dataset seed by defining the domain expertise, perspectives, topics, and examples for your domain-specific dataset. | |
| The dataset seed is then used to generate synthetic data for training a language model. | |
| """ | |
| ) | |
| st.markdown( | |
| """ | |
| ## 🗺️ The process | |
| ### Step 1: ~~Setup the project~~ | |
| ~~Define the project details, including the project name, domain, and API credentials. Create Dataset Repo on the Hub.~~ | |
| """ | |
| ) | |
| st.link_button("🚀 ~~Setup Project via the parent app~~", DIBT_PARENT_APP_URL) | |
| st.markdown( | |
| """ | |
| ### Step 2: Describe the Domain | |
| Define the domain expertise, perspectives, topics, and examples for your domain-specific dataset. | |
| You can collaborate with domain experts to define the domain expertise and perspectives. | |
| """ | |
| ) | |
| st.page_link( | |
| "pages/2_👩🏼🔬 Describe Domain.py", | |
| label="Describe Domain", | |
| icon="👩🏼🔬", | |
| ) | |
| st.markdown( | |
| """ | |
| ### Step 3: Generate Synthetic Data | |
| Use distilabel to generate synthetic data for your domain-specific dataset. | |
| You can run the pipeline locally or in this space to generate synthetic data. | |
| """ | |
| ) | |
| st.page_link( | |
| "pages/3_🌱 Generate Dataset.py", | |
| label="Generate Dataset", | |
| icon="🌱", | |
| ) | |
| st.markdown( | |
| """ | |
| ### Step 4: Review the Dataset | |
| Use Argilla to review the generated synthetic data and provide feedback on the quality of the data. | |
| """ | |
| ) | |
| st.link_button("🔍 Review the dataset in Argilla", ARGILLA_URL) | |