{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"Provides a Conditional Random Field (CRF) layer for TensorFlow 2 Keras models, enabling sequence labeling tasks with built-in support for masking and mixed precision training.","skillfed_tags":["sequence-labeling","nlp","abandoned"],"use_cases":["Named entity recognition (NER) tasks where label transitions matter and you want CRF constraints","Part-of-speech tagging or other sequence labeling where forbidden transitions should be penalized","Imbalanced NLP classification using the DSC loss variant to improve F1 scores","Prototyping sequence models with Keras when you need CRF without building it from scratch"],"what_it_does":"tf2crf wraps a CRF (Conditional Random Field) layer into TensorFlow 2 Keras, allowing you to add sequence-level constraints to neural sequence labeling models. It handles masking automatically and supports mixed precision training. The package includes a ModelWithCRFLoss wrapper that decouples the loss function from the layer itself, and offers an optional DSC (Dice) loss variant for imbalanced datasets.\n\nThe layer is designed to sit at the end of a sequence model (typically after a BiLSTM or BiGRU) and learns transition probabilities between output labels. You build a standard Keras model, wrap it with ModelWithCRFLoss, and train normally. However, the package has been abandoned since mid-2021, so you should verify compatibility with your TensorFlow and tensorflow-addons versions before relying on it in production.","worth_installing":"Yes-with-conditions. The package solves a real problem (CRF in Keras) and has low install friction, but it is abandoned and last tested with TensorFlow versions from 2021. Install only if you can verify compatibility with your TensorFlow and tensorflow-addons versions, and accept the risk that bugs or incompatibilities will not be fixed upstream. For active projects, consider whether a maintained alternative or a custom CRF implementation is safer."},"id":"tf2crf","links":{"html":"https://skillfed.io/packages/tf2crf","md":"https://skillfed.io/packages/tf2crf.md","pypi":"https://pypi.org/project/tf2crf/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2021-09-10","license_spdx":null,"license_treatment":"permissive","name":"tf2crf","python_support":"unspecified","summary":"a crf layer for tensorflow 2 keras"},"popularity":{"monthly_downloads":201989,"position":9654,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.33"}