Search Results for "work"

...See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you find label issues and other data issues, so you can train reliable ML models. All features of cleanlab work with any dataset and any model. Yes, any model: PyTorch, Tensorflow, Keras, JAX, HuggingFace, OpenAI, XGBoost, scikit-learn, etc. If you use a sklearn-compatible classifier, all cleanlab methods work out-of-the-box.

...Toloka entities are represented as Python classes. You can use them instead of accessing the API using JSON representations. There’s no need to validate JSON files and work with them directly. Support of both synchronous and asynchronous (via async/await) executions. Streaming support: build complex pipelines which send and receive data in real-time. For example, you can pass data between two related projects: one for data labeling, and another for its validation. AutoQuality feature which automatically finds the best fitting quality control rules for your project.

Adala is a data-centric AI framework focused on dataset curation, annotation, and validation. It helps AI teams manage high-quality training datasets by providing tools for data auditing, error detection, and quality assessment.

MetaErg is a stand-alone and fully automated metagenome and metaproteome annotation pipeline published at: https://www.frontiersin.org/articles/10.3389/fgene.2019.00999/full. If you are using this pipeline for your work, please cite: Dong X and Strous M (2019) An Integrated Pipeline for Annotation and Visualization of Metagenomic Contigs. Front. Genet. 10:999. doi: 10.3389/fgene.2019.00999 The instructions on configuring and running the MetaErg pipeline is available at GitHub repository: https://github.com/xiaoli-dong/metaerg

Speechalyzer: a tool for the daily work of a 'speech worker' It is optimized to process large speech data sets with respect to transcription, labeling and annotation. It is implemented as a client server based framework in Java and interfaces software for speech recognition, synthesis, speech classification and quality evaluation. The application is mainly the processing of training data for speech recognition and classification models and performing benchmarking tests on speech-to-text, text-to-speech and speech classification software systems.

We developed a RNA-Seq Data Annotation Pipeline named RNADAP, which measure genes expression in isoform level, work with high speed and less memory usage. Besides, our pipeline can be compatible with results from different mapping software.