Update README.md

README.md

@@ -188,7 +188,7 @@ In Figure 16, we show the progression of  accuracy with training for High Signal
 
 After manually inspecting fastText model-quality annotations and readability scores of large number of low-quality documents, we found that several abnormal documents were mislabeled by these annotators. We observed a peculiar pattern after tokenizing these documents: while most of these documents had similar lengths, they produced significantly different token counts. To quantify this effect, we propose novel annotations that effectively leverages information from the ``pre-tokenization'' stage (document char length, document size) and the ``post-tokenization'' stage (token counts) to identify potential low-quality documents. We refer to the the documents with extremely high or low number of tokens per character (or tokens per byte) as extreme-tokenized documents (see Figure 17 for a schematic).   
 
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+<img src="Extreme_ schematic.png" alt="Extreme_ schematic.png" style="width:1000px;"/>
 
 **Figure 17:** A schematic outlining the steps for removing extreme tokenized documents