generate info page from readme

README.md

@@ -10,23 +10,39 @@ pinned: false
 license: apache-2.0
 ---
 
-# Fact Checking rocks!   [![Generic badge](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue.svg)](https://huggingface.co/spaces/anakin87/fact-checking-rocks) [![Generic badge](https://img.shields.io/github/stars/anakin87/fact-checking-rocks?label=Github&style=social)](https://github.com/anakin87/fact-checking-rocks)
+# Fact Checking 🎸 Rocks!   [![Generic badge](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue.svg)](https://huggingface.co/spaces/anakin87/fact-checking-rocks) [![Generic badge](https://img.shields.io/github/stars/anakin87/fact-checking-rocks?label=Github&style=social)](https://github.com/anakin87/fact-checking-rocks)
 
 ## *Fact checking baseline combining dense retrieval and textual entailment*
 
 ### Idea 💡
-This project aims to show that a naive and simple baseline for fact checking can be built by combining dense retrieval and a textual entailment task (based on Natural Language Inference models).
+This project aims to show that a *naive and simple baseline* for fact checking can be built by combining dense retrieval and a textual entailment task (based on Natural Language Inference models).
+In a nutshell, the flow is as follows:
+* the users enters a factual statement
+* the relevant passages are retrieved from the knowledge base using dense retrieval
+* the system computes the text entailment between each relevant passage and the statement, using a Natural Language Inference model
+* the entailment scores are aggregated to produce a summary score.
+
+### System description 🪄
+This project is strongly based on [🔎 Haystack](https://github.com/deepset-ai/haystack), an open source NLP framework to realize search system. The main components of our system are an indexing pipeline and a search pipeline.
 
-### System description
-This project is strongly based on [Haystack](https://github.com/deepset-ai/haystack), an open source NLP framework to realize search system. The main components of our system are an indexing pipeline and a search pipeline.
 
 #### Indexing pipeline
 * [Crawling](https://github.com/anakin87/fact-checking-rocks/blob/321ba7893bbe79582f8c052493acfda497c5b785/notebooks/get_wikipedia_data.ipynb): Crawl data from Wikipedia, starting from the page [List of mainstream rock performers](https://en.wikipedia.org/wiki/List_of_mainstream_rock_performers) and using the [python wrapper](https://github.com/goldsmith/Wikipedia)
-* [Indexing through Haystack](https://github.com/anakin87/fact-checking-rocks/blob/321ba7893bbe79582f8c052493acfda497c5b785/notebooks/indexing.ipynb)
-  * Preprocess the downloaded documents into chunks consisting of 2 sentences
-  * Chunks with less than 10 words are discarded, because not very informative
-  * Instantiate a [FAISS](https://github.com/facebookresearch/faiss) Document store and store the passages on it
-  * Create embeddings for the passages, using a Sentence Transformer model and save them in FAISS. It seems that the retrieval task will involve [*asymmetric semantic search*](https://www.sbert.net/examples/applications/semantic-search/README.html#symmetric-vs-asymmetric-semantic-search) (statements to be verified are usually shorter than inherent passages), therefore I choose the model `msmarco-distilbert-base-tas-b`.
-  * Save FAISS index
+* [Indexing](https://github.com/anakin87/fact-checking-rocks/blob/321ba7893bbe79582f8c052493acfda497c5b785/notebooks/indexing.ipynb)
+  * preprocess the downloaded documents into chunks consisting of 2 sentences
+  * chunks with less than 10 words are discarded, because not very informative
+  * instantiate a [FAISS](https://github.com/facebookresearch/faiss) Document store and store the passages on it
+  * create embeddings for the passages, using a Sentence Transformer model and save them in FAISS. The retrieval task will involve [*asymmetric semantic search*](https://www.sbert.net/examples/applications/semantic-search/README.html#symmetric-vs-asymmetric-semantic-search) (statements to be verified are usually shorter than inherent passages), therefore I choose the model `msmarco-distilbert-base-tas-b`.
+  * save FAISS index
 
 #### Search pipeline
+
+* the user enters a factual statement
+* compute the embedding of the user statement using the same Sentence Transformer (`msmarco-distilbert-base-tas-b`)
+* retrieve the K most relevant text passages stored in FAISS (along with their relevance scores)
+* **text entailment task**: compute the text entailment between each text passage (premise) and the user statement (hypotesis), using a Natural Language Inference model (`microsoft/deberta-v2-xlarge-mnli`). For every text passage, we have 3 scores (summing to 1): entailment, contradiction, neutral. *(For this task, I developed a custom Haystack node: `EntailmentChecker`)*
+* aggregate the text entailment scores: compute the weighted average of them, where the weight is the relevance score. **Now it is possible to tell if the knowledge base confirms, is neutral or disproves the user statement.**
+* *empirical consideration: if in the first N documents (N<K),  there is a strong evidence of entailment/contradiction (partial aggregate scores > 0.5), it is better not to consider less relevant documents*
+
+### Limits and possible improvements ✨
+

Rock_fact_checker.py

@@ -30,7 +30,7 @@ def main():
     set_state_if_absent("raw_json", None)
     set_state_if_absent("random_statement_requested", False)
 
-    st.write("# Fact checking 🎸 Rocks!")
+    st.write("# Fact Checking 🎸 Rocks!")
     st.write()
     st.markdown(
         """

app_utils/frontend_utils.py

@@ -11,7 +11,7 @@ entailment_html_messages = {
 
 def build_sidebar():
     sidebar="""
-    <h1 style='text-align: center'>Fact checking 🎸 Rocks!</h1>
+    <h1 style='text-align: center'>Fact Checking 🎸 Rocks!</h1>
     <div style='text-align: center'>
     <i>Fact checking baseline combining dense retrieval and textual entailment</i>
     <p><br/><a href='https://github.com/anakin87/fact-checking-rocks'>Github project</a> - Based on <a href='https://github.com/deepset-ai/haystack'>Haystack</a></p>

pages/Info.py

@@ -1,4 +1,9 @@
 import streamlit as st
 from app_utils.frontend_utils import build_sidebar
 
-build_sidebar()
+build_sidebar()
+
+with open('README.md','r') as fin:
+    readme = fin.read().rpartition('---')[-1]
+
+st.markdown(readme, unsafe_allow_html=True)