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README.md

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----
-license: mit
----
+---
+language:
+- en
+pipeline_tag: tabular-classification
+tags:
+- Computational Neuroscience 
+license: mit
+---
+
+## Model description
+The model is trained on recordings from 11 mice in the V1, SC, and ALM brain regions using Neuropixels probes.
+Each recording was labeled by at least two independent annotators, with different combinations of labelers, achieving an 80% agreement rate.
+This model utilizes a subset of metrics that are computationally efficient while maintaining robust classification performance.
+
+# Intended use
+Used to identify Noise clusters automatically in SpikeInterface.
+
+# How to Get Started with the Model
+This can be used to automatically identify SUA units in spike-sorted outputs. If you have a sorting_analyzer, it can be used as follows:
+
+``` python
+    from spikeinterface.curation import auto_label_units
+    labels = auto_label_units(
+        sorting_analyzer = sorting_analyzer,
+        repo_id = "AnoushkaJain3/noise_neural_classifier_lightweight",
+        trusted = ['numpy.dtype']
+    )
+```
+
+## 📜 Citation  
+
+If you find [UnitRefine](https://github.com/anoushkajain/UnitRefine) useful in your research, please cite the following DOI:  
+**[10.6084/m9.figshare.28282841.v2](https://doi.org/10.6084/m9.figshare.28282841.v2)**.  
+
+We will be releasing a **preprint soon**. In the meantime, please use the above DOI for referencing.  
+
+## 🔗 Resources  
+
+- **GitHub Repository:** [UnitRefine](https://github.com/anoushkajain/UnitRefine)  
+- 📖 **SpikeInterface Tutorial – Automated Curation:**  
+  [View Here](https://spikeinterface.readthedocs.io/en/latest/tutorials_custom_index.html#automated-curation-tutorials)  
+
+UnitRefine is **fully integrated with SpikeInterface**, making it easy to incorporate into existing workflows. 🚀  
+
+
+# Authors
+
+Anoushka Jain and Chris Halcrow