Update README.md

README.md

@@ -53,8 +53,8 @@ Once loaded in SemanticFinder it takes around 2 seconds to search through the wh
 ### Create SemanticFinder files 
 
 1. Just use SemanticFinder as usual and run at least one search so that the index is created. This might take a while if your input is large. E.g. indexing the bible with 200 chars results in ~23k embeddings and takes 15-30 mins with a quantized gte-tiny model.
-2. Add the metadata (so other people can find your index) and export the file. Note that you have the freedom to reduce decimals to reduce file size; usually 3 is more than enough depending on the model. Less than 3 will also do in most cases but if you need highest accuracy go with 5 or more.
-3. Create a PR here if you want to see it added in the official collection! Just make sure to run `create_meta_data_csv_md.py` once to update the csv/md file. For now, the `readme.md` table here needs to be updated with the meta_data.md manually.
+2. Add the metadata (so other people can find your index) and export the file. Note that you have the freedom to reduce decimals to reduce file size; usually 2 is more than enough. Less than 2 will also do in most cases but if you need highest accuracy go with 5.
+3. Create a PR here if you want to see it added in the official collection! For this, upload the index file and this readme file. For generating the additional markdown table line, create an empty directory on your file system, move the index file and the python script `create_meta_data_csv_md.py` in there and run the python file with `python create_meta_data_csv_md.py`. It will create a csv and a markdown file. You can ignore the csv and just copy the table row line from the markdown file and add it to the table in this readme. That's it.
 
 ### Privacy
 - This repo is public and shares documents of public interest or documents in the public domain.
@@ -66,7 +66,10 @@ Once loaded in SemanticFinder it takes around 2 seconds to search through the wh
 Search for most similar words/sentences/paragraphs/pages in any text. Just imagine CTRL+F could find related words and not only the exact same one you used! 
 If you're working on the same text repeatedly you can save the index and reuse it.
 
-Also, there is the option of summarizing the results with generative AI like Qwen models right in your browser or connecting a heavy Llama2 instance with Ollama.
+Also, there is the option of summarizing the results with generative AI like Qwen models right in your browser or connecting any other LLM with Ollama.
+
+#### Creative use cases 
+- [Emoji Picker - Find the perfect emojis with semantic search](https://do-me.github.io/SemanticFinder/?hf=1910_Unicode_Emojis_9e4e14e7)
 
 #### Advanced use cases
 - [Translate words with multilingual embeddings](https://do-me.github.io/SemanticFinder/?hf=List_of_the_Most_Common_English_Words_70320cde&firstOnly=true&inferencingActive=False) or see which words out of a given list are most similar to your input word. Using e.g. the index of ~30k English words you can use more than 100 input languages to query! Note that here the expert settings change so that only the first match is displayed.