Practical overview
When it fits
Use it when a project needs custom or pretrained NER with a supported transformer encoder and token-label data, or when a Colab notebook is a useful way to explore the training workflow.
Who it is for
- NLP developers building NER models for domain-specific text
- Research teams working with token-labeled datasets
- Developers who want to prototype NER training or inference with Hugging Face resources
Documented uses
- Train a named-entity-recognition model on prepared text and labels.
- Identify and classify entity spans in text using a pretrained model.
- Explore linked SpanMarker models and their hosted inference widgets.
Requirements & limits
The project is a public GitHub repository under the Apache-2.0 license. It documents installation through pip and links to a Google Colab getting-started notebook.
- Training requires a dataset prepared with tokens and NER tags, plus a label set.
- The framework documents support for several common annotation schemes and encoder families.
- The repository includes model-loading and saving support, hyperparameter optimization, checkpointing and mixed-precision training through its Transformers foundation.
- Hosted inference widgets and Inference API options are associated with Hugging Face model pages, rather than being the same thing as the framework.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Load a pretrained model and predict entities in text
Use the documented Python inference pattern to obtain entity predictions from a pretrained Hub model.
Before you start
- The
span_markerPython module - A pretrained SpanMarker model on the Hugging Face Hub
Steps
Load a pretrained model with
SpanMarkerModel.from_pretrainedand its Hub model ID.Call
model.predictwith the text to analyze.Inspect the returned entity spans, labels, scores and character positions.
Expected outcome
The documented inference pattern returns predicted entities and their associated labels and positions.
Guide limitations
- Predictions depend on the selected pretrained model and its labels.
- The example demonstrates inference in Python; it does not establish suitability for a particular dataset or production deployment.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Spanmarkerner | Train a named-entity-recognition model on prepared text and labels. | Cloud | The project is a public GitHub repository under the Apache-2.0 license. It documents installation through pip and links to a Google Colab getting-started notebook. |
| Hyperpose | Customize pose-estimation architectures and datasets. | Docker, Linux, macOS, Windows | |
| Multi Task Nlp | Define multiple tasks and their data and model settings in a YAML file. | Linux, macOS, Windows | The project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- tomaarsen/SpanMarkerNER · Official homepage title · retained in its source language