🔑 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.
🔍 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.
📚 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.
💻 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.
📚 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.
📚 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.
💡 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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