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Update docs/source/training_guide.rst
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davebulaval authored Oct 6, 2023
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Expand Up @@ -33,7 +33,7 @@ Learning Successfully

Making a model converge is as much an art as it is a science since it often requires quite a bit of experimentation and parameter tuning. In the case
of fine-tuning, the models have already developed a base knowledge of the task that they were trained on, which gives them an edge.
This is especially true in the case of deepparse, since the task you are fine-tuning on remains exactly the same (i.e: parsing addresses).
This is especially true in the case of Deepparse since the task you are fine-tuning remains exactly the same (i.e. parsing addresses).
However, there are a couple of points to consider in order to obtain favourable results:

- **Make sure you have enough data**: deep learning models are notorious for being pretty data hungry, so unless you have enough data the models
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