sieves
Plug-and-play document AI with zero-shot models.
Master Loguru logging and observability in Sieves pipelines. Capture parsing errors and LLM outputs for better debugging and analysis. Learn how to integrate seamlessly.
How to Perform Information Extraction with Single vs Multi-Entity Mode in SievesMaster information extraction with Sieves single vs multi-entity modes. Learn when to use each for optimal accuracy and F1 scores in your NLP projects.
How to Handle Image Inputs with PIL Images in Sieves: A Complete GuideLearn to handle image inputs with PIL Images in Sieves. The Doc class allows direct processing of Pillow Image objects for multimodal pipelines. Explore visual data integration.
How to Export Results to HuggingFace Datasets Format in SievesEasily export Sieves results to HuggingFace Datasets format. Use to_hf_dataset() to create standard datasets with text and labels for local saving or pushing to the Hub.
How to Handle PII Masking and Anonymization in Sieves: A Complete Developer GuideLearn to handle PII masking and anonymization in Sieves. This guide explores the PIIMasking task for automatic PII detection, masking, and scoring with DSPy LangChain and Outlines.
How to Use the GLiNER2 Bridge for Named Entity Recognition in SievesLearn to use the GLiNER2 bridge for Named Entity Recognition in Sieves. Adapt the GLiNER2 model easily for efficient entity extraction without custom prompts. Get started today
How to Add Few-Shot Examples to Tasks in Sieves: A Complete GuideLearn how to add few-shot examples to Sieves tasks. Inject contextual examples into your model prompts by passing FewshotExample objects to PredictiveTask subclasses for enhanced performance.
How to Use Predictive Tasks in Sieves: NER, Classification, IE, RE, and QALearn how to use Sieves for NER, Classification, IE, RE, and QA. Unify predictive tasks with a common Pipeline interface for efficient execution.
How to Compose Pipelines Using the + Operator in Sieves: A Complete GuideLearn to compose Sieves pipelines with the + operator. Combine pipelines or append tasks for sequential execution and cache preservation. Get the complete guide.
How to Configure ModelSettings for Inference Modes and Batch Processing in SievesConfigure ModelSettings in Sieves for JSON or chain-of-thought inference modes and optimize throughput using the batch size parameter. Learn how to streamline your processing.
How to Access Raw Model Outputs and Debugging Meta Information in SievesAccess raw model outputs and debugging meta info in Sieves by setting include_meta=True. Capture LLM responses token usage and per chunk debug data in Doc.meta.
How to Track Token Usage and Input/Output Counts in SievesEasily track token usage input and output counts in your Sieves projects. Learn how Sieves automatically records valuable data for every model call within your Doc objects.
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