Explore and annotate datasets for machine learning
Organize and process datasets using AI
Upload files to a Hugging Face repository
Browse a list of machine learning datasets
Build datasets using natural language
Access NLPre-PL dataset and pre-trained models
Perform OSINT analysis, fetch URL titles, fine-tune models
Browse and view Hugging Face datasets
Organize and invoke AI models with Flow visualization
Find and view synthetic data pipelines on Hugging Face
Validate JSONL format for fine-tuning
Create datasets with FAQs and SFT prompts
Explore datasets on a Nomic Atlas map
VisuaLexNER is a powerful tool designed to help users explore and annotate datasets for machine learning. It is particularly tailored for Named Entity Recognition (NER) tasks, making it easier to create and manage high-quality training data. With VisuaLexNER, users can streamline the process of dataset creation, ensuring their data is well-organized and suitable for building accurate machine learning models.
What file formats does VisuaLexNER support for data import?
VisuaLexNER supports CSV, JSON, and plain text files for data import.
How are annotations exported from VisuaLexNER?
Annotations are exported in a structured format (e.g., JSON or CSV) with labeled entities clearly identified.
Can VisuaLexNER be used by teams for collaborative projects?
Yes, VisuaLexNER offers collaboration tools, allowing multiple users to work together on dataset creation and annotation.