Philip May on LinkedIn: #aicc #ihub #pix #vti #genai #llm #rag | 13 comments (2024)

I still remember very clearly how I trained a semantic bilingual German-English embedding model almost four years ago. Back then for T-Systems on site services GmbH. Nowadays, with the hype of #RAG, it is probably one of the most popular German-language open source models, measured by the more than one million downloads per month.This success fills me with joy and pride and also with some doubts. But why the doubts?I think it is important to understand that there is a big difference between semantic embeddings and Q/A retrieval models.Semantic embeddings can be used to cluster texts. Or, for example, to search large texts on the basis of a few keywords. However, they are less suitable for finding answers to questions.The reason is that the semantic similarity between a question and a text with the answer does not necessarily have to be high. For this reason, Q/A retrieval models that are trained to embed questions and potential answers close to each other are primarily suitable for retrieval in RAG systems.I'm afraid many are using my semantic embedding as a replacement for a Q/A retrieval model in a RAG application. This should not be done.At Telekom, we use self-trained Q/A retrieval models for our RAG retrieval. We also have our own data sets for this. For other EU languages, by the way, we have had very good experiences with the intfloat/multilingual-e5-large model. Incidentally, this also works very well for German.- my semantic bilingual German-English embedding called "T-Systems-onsite/cross-en-de-roberta-sentence-transformer": https://lnkd.in/eSx6kc6m- the "intfloat/multilingual-e5-large model" model: https://lnkd.in/e7aetC3tDeutsche Telekom #AICC #iHub #PIX #weloveai #VTI #GenAI #LLM

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I made a systematic comparison of the pandas file formats, compression methods and compression levels. The comparison is based on the compression rate and the save/load times.The article can be found here: https://lnkd.in/e6w7pWSXTL;RD:If you consider the compression method together with the compression level, zstd is the best option. This is especially true for compression levels 10 to 12.In terms of data format, Feather seems to be the best choice. Feather has a better compression ratio than Parquet. Up to a compression level of 12, the storage times of Parquet and Feather are practically the same. The loading times of Feather are definitely and significantly better than those of Parquet.For these reasons, Feather seems to be the best choice in combination with zstd and a compression level of 10 to 12.This can be done with:df.to_feather("filename.feather", compression="zstd", compression_level=10)

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Since some time I like #DVC but also #Jupyter #Notebooks. Because the DVC examples only ever show Python scripts, I always wondered how you can still use notebooks for the pipelines. Now this is the solution. Thanks for sharing Alaeddine Abdessalem!Deutsche Telekom, #AICC

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