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Add UnitRefine to the README

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  1. README.md +2 -1
README.md CHANGED
@@ -8,6 +8,7 @@ license: mit
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  ---
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  ## Model description
 
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  The model is trained on recordings from 11 mice in the V1, SC, and ALM brain regions using Neuropixels probes.
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  Each recording was labeled by at least two independent annotators, with different combinations of labelers, achieving an 80% agreement rate.
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  This model utilizes a subset of metrics that are computationally efficient while maintaining robust classification performance.
@@ -22,7 +23,7 @@ This can be used to automatically identify SUA units in spike-sorted outputs. If
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  from spikeinterface.curation import auto_label_units
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  labels = auto_label_units(
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  sorting_analyzer = sorting_analyzer,
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- repo_id = "AnoushkaJain3/noise_neural_classifier_lightweight",
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  trusted = ['numpy.dtype']
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  )
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  ```
 
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  ---
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  ## Model description
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+ This model is part of the UnitRefine project and it is a direct port of [this model](https://huggingface.co/AnoushkaJain3/noise_neural_classifier_lightweight).
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  The model is trained on recordings from 11 mice in the V1, SC, and ALM brain regions using Neuropixels probes.
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  Each recording was labeled by at least two independent annotators, with different combinations of labelers, achieving an 80% agreement rate.
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  This model utilizes a subset of metrics that are computationally efficient while maintaining robust classification performance.
 
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  from spikeinterface.curation import auto_label_units
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  labels = auto_label_units(
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  sorting_analyzer = sorting_analyzer,
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+ repo_id = "SpikeInterface/UnitRefine_noise_neural_classifier_lightweight",
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  trusted = ['numpy.dtype']
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  )
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  ```