final text

app.py

@@ -11,7 +11,7 @@ st.markdown("## The Gist")
 
 st.markdown("**Problem**🤔: Climate change misinformation spreads quickly and is difficult to combat. However, its important to do so, because climate change misinformation has direct impacts on public opinion and public policy surrounding climate change.")
 st.markdown("**Solution**💡: Develop a pipeline in which users can input climate change claims... and the pipeline returns whether the claim is refuted or supported by current climate science, along with the corresponding evidence.")
-st.markdown("**Approach**🔑")
+st.markdown("**Approach**🔑:")
 st.markdown("* There are many steps to this pipeline. Here, I focus on fine-tuning a transformer model, ClimateBERT, using the textual entailment task.")
 st.markdown("* The dataset used is Climate FEVER, a natural language inference dataset with 1,579 {claim, [evidence], [label]} tuples")
 st.markdown("* Given a {claim, evidence} pair, determine whether the climate claim is supported or refuted (or neither) by the evidence")
@@ -52,7 +52,7 @@ st.markdown("### How can Transformers Help?💡")
 st.markdown("**FEVER**")
 st.markdown("* FEVER, or Fact Extraction and VERification, was introduced in 2018 as the first dataset containing {fact, evdience, entailment_label} information. They extracted altering sentences from Wikipedia and had annotators report the relationship between the setences: entailment, contradition, not enough information.")
 st.markdown("* Since then, other researchers have expanded on this area in different domains")
-st.markdown("* Here, we use $Climate FEVER^3$, a similar dataset developed and annotated by ")
+st.markdown("* Here, we use Climate FEVER (3), a similar dataset developed and annotated by ")
 
 st.markdown("**Fact Verification / Fact-Checking")
 st.markdown("* This is simply an extenstion of the textual entailment task")
@@ -83,12 +83,19 @@ st.markdown("My work focuses on step 3 of the process: Training a transformer mo
 st.markdown("* evidence *supports* (entails) claim")
 st.markdown("* evidence *refutes* (contradicts) claim")
 st.markdown("* evidence *does not provide enough info to support or refute* (neutral) claim")
-st.markdown("For this project, I fine-tune $ClimateBERT_^4$ on the text entailment task.")
+st.markdown("For this project, I fine-tune ClimateBERT (4) on the text entailment task")
+
+st.markdown("### Try it out!")
+
 
 
 # section 6: analysis
+st.markdown("### Critical Analysis")
+st.markdown("What else could we do?")
+st.markdown("* Given more data, the performance of the model can be greatly improved. This is just a proof of concept")
+st.markdown("* This is only one small part of the puzzle!")
+st.markdown("In the complete pipeline (from user input to final output), we could move from just outputting evidence to training a transformer to reply with persuasive evidence. That is, instead of simply saying, \"This claim is supported by this evidence\", the model could transform the evidence into a persuasive argument, thus combatting climate change misinfo in a more platable and convincing way.")
 
-# section 7: conclusion
 
 # References + Resource Links
 st.markdown("### Resource Links")