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SpanMarker for Named Entity Recognition
| Date | Stars |
|---|---|
| 2026-07-24 | 477 |
| 2026-07-25 | 477 |
| 2026-07-28 | 477 |
| 2026-07-30 | 477 |
| 2026-08-06 | 477 |
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<div align="center">
<h1>
SpanMarker for Named Entity Recognition
</h1>
<a href="https://huggingface.co/tomaarsen/span-marker-roberta-large-ontonotes5" target="_blank">
<img src="https://github.com/tomaarsen/SpanMarkerNER/assets/37621491/c76d6393-bb0b-44c3-9412-fd9c8313dcc1">
</a>
[🤗 Models](https://huggingface.co/models?library=span-marker) |
[🛠️ Getting Started In Google Colab](https://colab.research.google.com/github/tomaarsen/SpanMarkerNER/blob/main/notebooks/getting_started.ipynb) |
[📄 Documentation](https://tomaarsen.github.io/SpanMarkerNER) | 📊 [Thesis](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
</div>
SpanMarker is a framework for training powerful Named Entity Recognition models using familiar encoders such as BERT, RoBERTa and ELECTRA.
Built on top of the familiar [🤗 Transformers](https://github.com/huggingface/transformers) library, SpanMarker inherits a wide range of powerful functionalities, such as easily loading and saving models, hyperparameter optimization, automatic logging in various tools, checkpointing, callbacks, mixed precision training, 8-bit inference, and more.
<!--Tightly implemented on top of the [🤗 Transformers](https://github.com/huggingface/transformers/) library, SpanMarker can take advantage of its valuable functionality.-->
<!-- like performance dashboard integration, automatic mixed precision, 8-bit inference-->
Based on the [PL-Marker](https://arxiv.org/pdf/2109.06067.pdf) paper, SpanMarker breaks the mold through its accessibility and ease of use. Crucially, SpanMarker works out of the box with many common encoders such as `bert-base-cased`, `roberta-large` and `bert-base-multilingual-cased`, and automatically works with datasets using the `IOB`, `IOB2`, `BIOES`, `BILOU` or no label annotation scheme.
Additionally, the SpanMarker library has been integrated with the Hugging Face Hub and the Hugging Face Inference API. See the SpanMarker documentation on [Hugging Face](https://huggingface.co/docs/hub/span_marker) or see [all SpanMarker models on the Hugging Face Hub](https://huggingface.co/models?library=span-marker).
Through the Inference API integration, users can test any SpanMarker model on the Hugging Face Hub for free using a widget on the [model page](https://huggingface.co/tomaarsen/span-marker-bert-base-fewnerd-fine-super). Furthermore, each public SpanMarker model offers a free API for fast prototyping and can be deployed to production using Hugging Face Inference Endpoints.
| Inference API Widget (on a model page) | Free Inference API (`Deploy` > `Inference API` on a model page) |
| ------------- | ------------- |
|  |  |
## Documentation
Feel free to have a look at the [documentation](https://tomaarsen.github.io/SpanMarkerNER).
## Installation
You may install the [`span_marker`](https://pypi.org/project/span-marker) Python module via `pip` like so:
```
pip install span_marker
```
## Quick Start
### Training
Please have a look at our [Getting Started](notebooks/getting_started.ipynb) notebook for details on how SpanMarker is commonly used. It explains the following snippet in more detail. Alternatively, have a look at the [training scripts](training_scripts) that have been successfully used in the past.
| Colab | Kaggle | Gradient Excerpt of 16,855 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:63c4deee5ecdc512, topic:nlp, topic:spacy, desc:named entity recognition