fixed review comments in the sample notebook for text classifier
"source": [
"## Model metrics\n",
"\n",
- "To get a sense of how well the model is trained, we will calculate some important metrics for our `text-classifier` model. First, we will see what's the model accuracy[[2]](#References) and error rate on the entire data-set. We will call the model's `accuracy()` method."
+ "To get a sense of how well the model is trained, we will calculate some important metrics for our `text-classifier` model. First, to find how accurate[[2]](#References) the model is in correctly predicting the classes in the dataset, we will call the model's `accuracy()` method."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Other important metrics to look at are Precision, Recall & F-measures [[3]](#References). To find `precision`, `recall` & `f1` scores per label/class we will call the model's `metrics_per_label()` method."
+ "Other important metrics to look at are Precision, Recall & F1-measures [[3]](#References). To find `precision`, `recall` & `f1` scores per label/class we will call the model's `metrics_per_label()` method."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "In this notebook we will build a classifier using `TextClassifier` class of `arcgis.learn.text` module. The dataset consisted of house addresses of 10 countries written in languages like English, Japanese, French, Spanish, etc. To achieve this we used a [multi-lingual transformer backbone](https://huggingface.co/transformers/v3.0.2/multilingual.html) like `XLM-RoBERTa` to build a classifier to predict the country for an input house address. "
+ "In this notebook, we have built a text classifier using `TextClassifier` class of `arcgis.learn.text` module. The dataset consisted of house addresses of 10 countries written in languages like English, Japanese, French, Spanish, etc. To achieve this we used a [multi-lingual transformer backbone](https://huggingface.co/transformers/v3.0.2/multilingual.html) like `XLM-RoBERTa` to build a classifier to predict the country for an input house address. "
]
},
{
"\n",
"[2] [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision)\n",
"\n",
- "[3] [Precision, recall and F-measures](https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-and-f-measures)"
+ "[3] [Precision, recall and F1-measures](https://scikit-learn.org/stable/modules/model_evaluation.html#precision-recall-and-f-measures)"
]
}
],
