Advantages of BGE embeddings for Retrieval Augmented Generation
This video discusses the development of BGE embeddings and their advantages for retrieval augmented generation.
00:00:05 This video discusses the development of BGE embeddings and their role in retrieval augmented generation. It also mentions the Massive Text Embedding Benchmark where BGE embeddings perform well.
🔑 BGE embeddings are a new development in the embedding space that fit into the retrieval augmented generation space.
⚙️ BG embeddings are used to create vector stores for retrieval augmented generation, where large llm models are used to produce contextual answers.
🏆 BG embeddings perform well in the Massive Text Embedding Benchmark, ranking highly in tasks like clustering, re-ranking, and semantic textual similarity.
00:02:20 This video discusses the advantages of BGE embeddings for retrieval augmented generation, including their small size and superior performance compared to commercial models. It also explores the use of flag embedding and retro me for pre-training and fine-tuning, as well as the connectivity with Lang chain library. The video concludes with a demonstration of pre-training in a collab notebook.
🔍 BGE embeddings outperform open ai's text embedding ada002.
⚙️ Flag embedding is used to train the models.
🔗 BG embeddings have connectivity with other libraries like Lang chain.
00:04:35 This video discusses the use of BGE embeddings for retrieval augmented generation using a dataset of IPO documents.
📚 The speaker created a dataset from IPO documents for analysis.
💼 The dataset contains OCR text from 500-page IPO prospectus documents.
💡 The dataset can be used to train a model for various industries.
00:06:50 This video demonstrates how to use BGE embeddings for retrieval augmented generation, including installation and data preparation.
💻 The video discusses the process of fetching data using Hugging Face and installing necessary libraries.
📊 The data set, focused on IPO prospectus, is split into train and test sets, with the test set being used for analysis.
🔍 The OCR text and content pages of the prospectus are retrieved and split into smaller chunks for analysis.
00:09:06 This video explains the process of extracting data from datasets, creating a new dataset in JSON format, and pre-training a model using configurable parameters.
📚 The video discusses the process of extracting and organizing data sets for retrieval augmented generation.
💻 Json line format is introduced as a way to store data sets in separate lines in a Json format.
⚙️ The pre-training process involves specifying configurable parameters and monitoring the loss, which decreases over time.
00:11:22 The video discusses the use of BGE embeddings for retrieval augmented generation and compares the similarity of two sentences using these embeddings.
📚 The video discusses the use of state-of-the-art BGE embeddings for retrieval augmented generation.
💻 The speaker saves pre-trained embeddings and compares the similarity between two sentences using the BGE base embeddings.
✅ The results show that the embeddings indicate a high level of similarity between the sentences.
00:13:38 Creating and comparing custom embeddings with BGE base model, discussing training limitations and alternatives.
💡 Creating custom embeddings and comparing them to the base model.
⚠️ Use a machine with sufficient GPU memory for training the model.
🔎 Tips for training the model: use smaller models and batch sizes to pre-train faster.
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