fixed references for the text classifier guide
"source": [
"## Finding optimum learning rate\n",
"\n",
- "In machine learning, the `learning rate`[1] is a tuning parameter that determines the step size at each iteration while moving toward a minimum of a loss function, it metaphorically represents the speed at which a machine learning model \"learns\"\n",
+ "In machine learning, the `learning rate`[6] is a tuning parameter that determines the step size at each iteration while moving toward a minimum of a loss function, it metaphorically represents the speed at which a machine learning model \"learns\"\n",
"\n",
"- If the learning rate is low, then model training will take a lot of time because steps towards the minimum of the loss function are tiny.\n",
"- If the learning rate is high, then training may not converge or even diverge. Weight changes can be so big that the optimizer overshoots the minimum and makes the loss worse.\n",
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"metadata": {},
"source": [
- "Other important metrics to look at are Precision, Recall & F-measures [6].\n",
+ "Other important metrics to look at are Precision, Recall & F-measures [7].\n",
"\n",
"Here is a brief description of them:\n",
"- **Precision** - Precision talks about how precise/accurate your model is. Out of those predicted positive, how many of them are actual positive. \n",
"\n",
"[5] [BERT Paper](https://arxiv.org/pdf/1810.04805.pdf)\n",
"\n",
- "[6] [Precision, recall and F-measures](https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-and-f-measures)"
+ "[6] [Learning Rate](https://en.wikipedia.org/wiki/Learning_rate)\n",
+ "\n",
+ "[7] [Precision, recall and F-measures](https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-and-f-measures)"
]
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- "version": "3.6.10"
+ "version": "3.6.9"
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