tutorial: minor descriptions in tutorial

docs/tutorials/filter_corpus_using_quality.ipynb

@@ -61,7 +61,7 @@
    "source": [
     "\n",
     "### The Data\n",
-    "For our first example we will filter web content. For this we will use the [mC4 dataset](https://huggingface.co/datasets/mc4). It would take ages to download the whole data thus we will stream down 1000 samples from the dataset."
+    "For our first example we will filter web content. For this we will use the [mC4 dataset](https://huggingface.co/datasets/mc4). It would take ages to download the whole data so instead we will stream down 1000 samples from the dataset."
    ]
   },
   {
@@ -78,7 +78,7 @@
     "# download the first 1 000\n",
     "dataset = dataset.take(1000)\n",
     "\n",
-    "# extract the text and remove texts which are too long\n",
+    "# extract the text\n",
     "texts = [sample [\"text\"] for sample in dataset]\n"
    ]
   },
@@ -108,7 +108,7 @@
    "source": [
     "### Filtering\n",
     "\n",
-    "To filter domains using `textdescriptives` we need to first set up the pipeline:"
+    "To filter texts using `textdescriptives` we need to first set up the pipeline:"
    ]
   },
   {
@@ -211,7 +211,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It seems like this documents did no pass the quality check. Let us examine why that is:"
+    "It seems like this document did no pass the quality check. Let us examine why that is:"
    ]
   },
   {
@@ -287,7 +287,7 @@
    "metadata": {},
    "source": [
     "### Extracting high quality texts\n",
-    "We are typically interested in text which are not of low quality. Thus we can extract these by filtering out the text which did not pass the quality check."
+    "We are typically interested in text which are not of low quality. We can extract these by filtering out the texts which did not pass the quality check."
    ]
   },
   {
@@ -355,7 +355,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you want to read more about what each argument does, please check out the [documentation](https://hlasse.github.io/TextDescriptives/quality.html#data-classes)."
+    "If you want to read more about what each argument does, please check out the [documentation](https://hlasse.github.io/TextDescriptives/quality.html#data-classes).\n",
+    "All the `passed` values and `passed_quality_check` attributes are dynamically updated when you can `.set_quality_thresholds`."
    ]
   },
   {
@@ -405,7 +406,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "We can donwload the dataset using the following command:"
+    "We can download the dataset using the following command:"
    ]
   },
   {
@@ -745,7 +746,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can for example see that the speech dataset contains notably fewer samples than the others. For that reason, let's only concern ourself with `legal` and `hest`"
+    "We can for example see that the speech dataset contains notably fewer samples than the others. "
    ]
   },
   {
@@ -754,10 +755,10 @@
    "metadata": {},
    "source": [
     "### Quality Filtering\n",
-    "After we have prepared our datasets we can now start with the quality filtering. Using Textdescriptives this is extremely simple. We need to do 3 things:\n",
+    "After we have prepared our datasets we can now start with the quality filtering. Using TextDescriptives, this is extremely simple. We need to do 3 things:\n",
     "\n",
     "1) Create a pipeline\n",
-    "2) Add the quality component from textdescriptives to it\n",
+    "2) Add the quality component to it\n",
     "3) Apply the pipeline to the dataset\n"
    ]
   },
@@ -897,7 +898,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we see that fraction of characters which is a part of a duplicate 10 gram is >50%. This is a reason why the sample was filtered out. This is not uncommon for legal documents which contain a lot of standard phrases. However you might wish to change the threshold for this filter. We showed you have to do this in the previous section. We also see that the `alpha_ratio` is close 0.8. This means that the text is mostly made up of alphabetic characters. This is good, but as we will see later by no mean common for legal texts."
+    "Here we see that fraction of characters which is a part of a duplicate 10 gram is >50%. This is a reason why the sample was filtered out. This is not uncommon for legal documents which contain a lot of standard phrases. However you might wish to change the threshold for this filter. We showed you have to do this in the previous section. We also see that the `alpha_ratio` is close 0.8. This means that the text is mostly made up of alphabetic characters. This is good, but as we will see later, this is not common for legal texts."
    ]
   },
   {
@@ -906,7 +907,7 @@
    "metadata": {},
    "source": [
     "### Filtering out the text\n",
-    "Assuming we don't want to change the filter we can now use it to filter out the texts that we want to keep:"
+    "Assuming we don't want to change the filters we can now use it to filter out the texts that we want to keep:"
    ]
   },
   {
@@ -941,7 +942,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That seems like a lot, we should probably check why that is. We can do this by looking at the distribution of the scores:"
+    "That seems like a lot, we should probably check why that is. We can do this by looking at the distribution of the scores of e.g. duplicate 10-gram fraction:"
    ]
   },
   {
@@ -1025,7 +1026,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that most of the text does not pass the `alpha_ratio` threshold of 0.7 or higher. This is not uncommon for legal documents as e.g. the paragraph sign `§` is not an alphabetic character. It might be relevant to change the threshold to 0.7 or lower."
+    "We see that most of the text do not pass the `alpha_ratio` threshold of 0.7 or higher. This is not uncommon for legal documents as e.g. the paragraph sign `§` is not an alphabetic character. It might be relevant to change the threshold to 0.7 or lower."
    ]
   },
   {