Update README (#33)

README.md

@@ -1,44 +1,22 @@
-# Buster, the Mila cluster chatbot!
+# Buster, the QA documentation chatbot!
 
-Buster is a chatbot that can answer questions about the Mila cluster. You can try it [here](https://huggingface.co/spaces/jerpint/buster).
+Buster is a question-answering chatbot that can be tuned to specific documentations. You can try it [here](https://huggingface.co/spaces/jerpint/buster), where it will answer questions about [🤗 Transformers](https://huggingface.co/docs/transformers/index).
 
 
-![Question about tmpdir](buster/imgs/qa_tmpdir.png)
-<!---
-Where should I put my data when running a job?
--->
+![Question: How do I load a Huggingface model?](buster/imgs/qa_web_load.png)
 
-![Question about GPUs jobs](buster/imgs/qa_gpus.png)
-<!---
-How do I run a job with 2 GPUs?
--->
+![Question: My code is crashing with "CUDA out of memory". What can I do to solve this?](buster/imgs/qa_web_oom.png)
 
-It also works in french, although not as well:
-![Question en français](buster/imgs/qa_french.png)
-<!---
-Comment est-ce que je lance une job avec 2 GPUs?
--->
+## How does Buster work?
 
-If you ask a stupid question, you will get a stupid answer:
-![Dumb question](buster/imgs/qa_pasta.png)
-<!---
-What has more calories: a DGX A100 or a pasta salad?
--->
+First, we parsed the documentation into snippets. For each snippet, we obtain an embedding by using the [OpenAI API](https://beta.openai.com/docs/guides/embeddings/what-are-embeddings).
 
-## How does Buster works?
-
-First, we parsed the [Mila docs](https://docs.mila.quebec/index.html) into snippets. You can see exactly how in [`buster.docparser.get_all_documents`](https://github.com/jerpint/buster/blob/main/buster/docparser.py#L17).
-
-The resulting snippets can be seen in [`buster/data/documents.csv`](https://github.com/jerpint/buster/blob/main/buster/data/documents.csv).
-
-For each snippet, we obtain an embedding by using the [OpenAI API](https://beta.openai.com/docs/guides/embeddings/what-are-embeddings).
-
-Then, when a user asks a question, we compute its embedding, find the snippet from the doc with the highest cosine similarity to the question.
+Then, when a user asks a question, we compute its embedding, and find the snippets from the doc with the highest cosine similarity to the question.
 
 Finally, we craft the prompt:
-- The most relevant snippet from the doc
-- The engineering prompt: "Now answer the following question:"
-- The user's question
+- The most relevant snippets from the doc.
+- The engineering prompt.
+- The user's question.
 
 We send the prompt to the [OpenAI API](https://beta.openai.com/docs/api-reference/completions), and display the answer to the user!