Practical overview
When it fits
Use Nlpnet when a Python workflow needs one or more of its documented tagging, semantic role labeling or dependency parsing functions.
Who it is for
- Python developers building natural-language processing workflows
- Researchers and practitioners who need the three supported NLP tasks
- Developers working with Portuguese text and the corresponding trained models
Documented uses
- Assign part-of-speech tags to text
- Identify semantic roles
- Parse dependency relationships
Requirements & limits
The public repository identifies the project as MIT-licensed.
- NLTK, NumPy and NLTK data are required; the documentation specifically calls for the Punkt tokenizer.
- Portuguese trained models require a separate model-data download.
- A C compiler is needed for C extensions. The README notes that Windows may require extra compiler setup.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare Nlpnet dependencies and Portuguese model data
Set up the documented NLP and model-data prerequisites before using Nlpnet.
Before you start
- Python environment
- NLTK and NumPy
- A C compiler for the C extensions
- Portuguese model data if using trained Portuguese models
Steps
Install Nlpnet and its NLTK and NumPy dependencies.
Download NLTK data and select the Punkt tokenizer in the NLTK download interface.
If working with trained Portuguese models, download the model data from the project’s model page.
Expected outcome
The documented dependencies, tokenizer data and, when relevant, Portuguese model files are in place for subsequent use.
Guide limitations
- Windows may require additional compiler configuration; the README mentions MinGW as an alternative to the Microsoft compiler.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- erickrf/nlpnet · Official homepage title · retained in its source language