Update README.

PiperOrigin-RevId: 745457084

Author: Hilly12

README.md

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+# RecML: High-Performance Recommender Library
+
+## Vision
+
+RecML is envisioned as a high-performance, large-scale deep learning recommender
+system library optimized for Cloud TPUs. It aims to provide researchers and
+practitioners state-of-the-art reference implementations, tools, and best
+practice guidelines for building and deploying recommender systems.
+
+The key goals of RecML are:
+
+*   **Performance & Scalability:** Leverage Cloud TPUs (including SparseCore
+    acceleration) to deliver exceptional performance for training and serving
+    massive models with large embeddings on datasets with millions or billions
+    of items/users. RecML can additionally target Cloud GPUs.
+*   **State-of-the-Art Models:** Provide production-ready, easy-to-understand
+    reference implementations of popular and cutting-edge models, with a strong
+    focus on LLM-based recommenders.
+*   **Ease of Use:** Offer a user-friendly API, intuitive abstractions, and
+    comprehensive documentation/examples for rapid prototyping and deployment.
+*   **Flexibility:** Primarily built with Keras and JAX, but designed with
+    potential future expansion to other frameworks like PyTorch/XLA.
+*   **Open Source:** Foster community collaboration and provide components to
+    help users get started with advanced recommender workloads on Google Cloud.
+
+## Features
+
+*   **High Performance:** Optimized for Cloud TPU (SparseCore) training and
+    inference.
+*   **Scalable Architecture:** Designed for massive datasets and models with
+    large embedding tables. Includes support for efficient data loading
+    (tf.data, potentially Grain) and sharding/SPMD.
+*   **State-of-the-Art Model Implementations:** Reference implementations for
+    various recommendation tasks (ranking, retrieval, sequential).
+*   **Reusable Building Blocks:**
+    *   Common recommendation layers (e.g., DCN, BERT4Rec).
+    *   Specialized Embedding APIs (e.g. JAX Embedding API for SparseCore).
+    *   Standardized metrics (e.g., AUC, Accuracy, NDCG@K, MRR, Recall@K).
+    *   Common loss functions.
+*   **Unified Trainer:** A high-level trainer abstraction capable of targeting
+    different hardware (TPU/GPU) and frameworks. Includes customizable training
+    and evaluation loops.
+*   **End-to-End Support:** Covers aspects from data pipelines to training,
+    evaluation, checkpointing, metrics logging (e.g., to BigQuery), and model
+    export/serving considerations.
+
+## Models Included
+
+This library aims to house implementations for a variety of recommender models,
+including:
+
+*   **SASRec:** Self-Attention based Sequential Recommendation
+*   **BERT4Rec:** Bidirectional Encoder Representations from Transformer for
+    Sequential Recommendation.
+*   **Mamba4Rec:** Efficient Sequential Recommendation with Selective State
+    Space Models.
+*   **HSTU:** Hierarchical Sequential Transduction Units for Generative
+    Recommendations.
+*   **DLRM v2:** Deep Learning Recommendation Model
+
+## Roadmap / Future Work
+
+*   Expand reference model implementations (Retrieval, Uplift, foundation user
+    model).
+*   Add support for optimized configurations and lower precision training
+    (bfloat16, fp16).
+*   Improve support for Cloud GPU training and inference
+*   Enhance sharding and quantization support.
+*   Improve integration with Keras (and Keras Recommenders) and potentially
+    PyTorch/XLA.
+*   Develop comprehensive model serving examples and integrations.
+*   Refine data loading pipelines (e.g., Grain support).
+*   Add more common layers, losses, and metrics.
+
+## Responsible Use
+
+As with any machine learning model, potential risks exist. The performance and
+behavior depend heavily on the training data, which may contain biases reflected
+in the recommendations. Developers should carefully evaluate the model's
+fairness and potential limitations in their specific application context.
+
+## License
+
+RecML is released under the Apache 2.0. Please see the `LICENSE` file for full
+details.