The following repository contains a collected set of files that are used to create a text summarizer.
Simply provide a URL to a text you want summarized and a question you want answered and the model will do the rest.
Example:
Reliance on third party services:
The primary model used is Ollama taken from the following site: Ollama. Furthermore, the repository uses LangChain to serve the model and a web interface for the user is provided via Streamlit.
A CPU, the model is quantized and optimized for CPU, Metal, and CUDA by the awesome team behind ggml
The following installation steps are required to run the project:
- Clone the repository
- Install the requirements
- Setup Ollama
- Install Ollama
- Pull the model: "llama2-7b" (chat) (CMD:
ollama pull llama2:latest(fine-tuned 7b parm model) - Use the model file to create a QA model called "summarizev2" (CMD:
ollama create summarizev2 -f ./Modelfile)
- Either run the model via the command line or via the web interface
command line: locate the repositorry folder and type the following in the command line.
Example usage: python -m src.main --url="https://en.wikipedia.org/wiki/Francisco_Goya" --question="Who was Goya?"
Arguments
Use python main.py --help to see the full list of arguments:
--url- the url of the text to summarize--question- the question to ask the model--model- the model to use for summarization--base-url- the base url for Ollama--verbose- if True, print out debug information--chunk-size- size of chunks to split text into--embedding-model- embedding model to use--retriever- retriever to use--device- device to use
web interface:
locate the file streamlit_app.py and run it via the command line.
Example usage: streamlit run streamlit_app.py. This should result in a web interface being opened in your browser. It should look similar to the image provided above.