Search Results for "natural language processing"
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Showing 4 open source projects for "natural language processing"
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PostgresML
The GPU-powered AI application database
...Combine and automate the entire workflow from embedding generation to indexing and querying for the simplest (and fastest) knowledge-based chatbot implementation. Leverage multiple types of natural language processing and machine learning models such as vector search and personalization with embeddings to improve search results. Leverage your data with time series forecasting to garner key business insights. Build statistical and predictive models with the full power of SQL and dozens of regression algorithms. Return results and detect fraud faster with ML at the database layer. ... -
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rust-bert
Rust native ready-to-use NLP pipelines and transformer-based models
rust-bert is a Rust-based implementation of transformer-based natural language processing models that provides ready-to-use pipelines for tasks such as text classification, summarization, and question answering. The project ports many capabilities of the Hugging Face Transformers ecosystem into the Rust programming language. It allows developers to run state-of-the-art NLP models like BERT, GPT-2, and DistilBERT directly within Rust applications while maintaining high performance and memory efficiency. ... -
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SIG Rust
Rust language bindings for TensorFlow
SIG Rust provides idiomatic Rust bindings for TensorFlow, making it possible for developers to work with TensorFlow functionality from within the Rust programming language. Rather than replacing TensorFlow itself, it acts as an integration layer that connects Rust applications to the TensorFlow C API. The repository is designed for developers who want Rust’s performance, safety, and systems programming strengths while still accessing TensorFlow’s machine learning capabilities. It includes... -
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Weld
High-performance runtime for data analytics applications
...Weld is particularly useful for workloads involving large-scale data processing in frameworks such as NumPy, Spark, and TensorFlow. The language includes built-in constructs for expressing data-parallel operations, enabling efficient execution on modern hardware architectures. By combining operations from multiple libraries into a single optimized execution plan, Weld can significantly improve performance in analytics and machine learning pipelines. -
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