Improved README

README.md

@@ -11,6 +11,7 @@ license: Apache-2.0
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 # Who killed Laura Palmer?   [![Generic badge](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue.svg)](https://huggingface.co/spaces/anakin87/who-killed-laura-palmer) [![Generic badge](https://img.shields.io/github/stars/anakin87/who-killed-laura-palmer?label=Github&style=social)](https://github.com/anakin87/who-killed-laura-palmer)
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 ## 🗻🗻 Twin Peaks Question Answering system
 
 WKLP is a simple Question Answering system, based on data crawled from [Twin Peaks Wiki](https://twinpeaks.fandom.com/wiki/Twin_Peaks_Wiki). It is built using [🔍 Haystack](https://github.com/deepset-ai/haystack), an awesome open-source framework for building search systems that work intelligently over large document collections.
@@ -29,3 +30,23 @@ WKLP is a simple Question Answering system, based on data crawled from [Twin Pea
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 ## What can I learn from this project? 📚
+- How to quickly ⌚ build a modern Question Answering system using [🔍 Haystack](https://github.com/deepset-ai/haystack)
+- How to generate questions based on your documents
+- How to build a nice [Streamlit](https://github.com/streamlit/streamlit) web app to show your QA system
+- How to optimize the web app to 🚀 deploy in [🤗 Spaces](https://huggingface.co/spaces)
+
+## Repository structure 📁
+- [app.py](./app.py): Streamlit web app
+- [app_utils folder](./app_utils/): python modules used in the web app
+- [crawler folder](./crawler/): Twin Peaks crawler, developed with Scrapy and fandom-py
+- [notebooks folder](./notebooks/): Jupyter/Colab notebooks to create the Search pipeline and generate questions (using Haystack)
+- [data folder](./data/): all necessary data
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+Within each folder, you can find more in-depth explanations.
+
+## Possible improvements ✨
+- The reader model (`deepset/roberta-base-squad2`) is a good compromise between speed and accuracy, running on CPU. There are certainly better (and more computationally expensive) models, as you can read in the [Haystack documentation](https://haystack.deepset.ai/pipeline_nodes/reader).
+- You can also think about preparing a Twin Peaks QA dataset and fine-tune the reader model to get better accuracy, as explained in [Haystack tutorial](https://haystack.deepset.ai/tutorials/fine-tuning-a-model).
+- ...
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+