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- (docs) fix a typo
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docs/exportingmodels.md

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@@ -81,7 +81,7 @@ and you should see new files like `MyModelNameGoesHere_simplified_fp16.onnx` in
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## PyTorch
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This is the simplest of the export methods because, as mentioned in [ONNXExporter](#onnxexporter) section, pySIPFENN
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This is the simplest of the export methods because, as mentioned in [ONNXExporter](#ONNXExporter) section, pySIPFENN
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models are already stored as PyTorch models; therefore, no conversion is needed. You can use it by simply calling
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from pysipfenn import PyTorchExporter
@@ -106,7 +106,7 @@ other platforms as well, such as Linux or Windows, through [coremltools](https:/
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from Apple used by this exporter.
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Note that under the hood, CoreML uses the float16 precision, so the model predictions will numerically match those
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exported with [ONNXExporter](#onnxexporter) in float16 precision rather than the default pySIPFENN models. This can
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exported with [ONNXExporter](#ONNXExporter) in float16 precision rather than the default pySIPFENN models. This can
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be useful if you want to use the models on devices with limited memory, such as mobile phones or embedded devices, and
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generally should not significantly affect the accuracy of the predictions.
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