π BGE embeddings are a new development in the embedding space that fit into the retrieval augmented generation space.
βοΈ BG embeddings are used to create vector stores for retrieval augmented generation, where large llm models are used to produce contextual answers.
π BG embeddings perform well in the Massive Text Embedding Benchmark, ranking highly in tasks like clustering, re-ranking, and semantic textual similarity.
π BGE embeddings outperform open ai's text embedding ada002.
βοΈ Flag embedding is used to train the models.
π BG embeddings have connectivity with other libraries like Lang chain.
π The speaker created a dataset from IPO documents for analysis.
πΌ The dataset contains OCR text from 500-page IPO prospectus documents.
π‘ The dataset can be used to train a model for various industries.
π» The video discusses the process of fetching data using Hugging Face and installing necessary libraries.
π The data set, focused on IPO prospectus, is split into train and test sets, with the test set being used for analysis.
π The OCR text and content pages of the prospectus are retrieved and split into smaller chunks for analysis.
π The video discusses the process of extracting and organizing data sets for retrieval augmented generation.
π» Json line format is introduced as a way to store data sets in separate lines in a Json format.
βοΈ The pre-training process involves specifying configurable parameters and monitoring the loss, which decreases over time.
π The video discusses the use of state-of-the-art BGE embeddings for retrieval augmented generation.
π» The speaker saves pre-trained embeddings and compares the similarity between two sentences using the BGE base embeddings.
β The results show that the embeddings indicate a high level of similarity between the sentences.
π‘ Creating custom embeddings and comparing them to the base model.
β οΈ Use a machine with sufficient GPU memory for training the model.
π Tips for training the model: use smaller models and batch sizes to pre-train faster.
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