Update to families.csv using a custom ML model

.ci/tags.html

@@ -213,29 +213,32 @@
   }
 
   class FontTag {
-    constructor(name, axes, category, score = 0) {
+    constructor(name, axes, category, score = 0, providence = "") {
         this.name = name;
         this.axes = axes;
         this.category = category;
         this.score = score;
+        this.providence = providence;
     }
     static fromCsv(line) {
-      const [name, axes, category, score] = parseCSVLine(line);
-      return new FontTag(name, axes, category, score);
+      const [name, axes, category, score, providence] = parseCSVLine(line);
+      return new FontTag(name, axes, category, score, providence || "");
     }
     isVF() {
       return this.axes.length > 0;
     }
 
     toCsv() {
+        const prov = this.providence ? `,${this.providence}` : "";
         if (this.axes.includes(",")) {
-          return `${this.name},"${this.axes}",${this.category},${this.score}`;
+          return `${this.name},"${this.axes}",${this.category},${this.score}${prov}`;
         }
-        return `${this.name},${this.axes},${this.category},${this.score}`;
+        return `${this.name},${this.axes},${this.category},${this.score}${prov}`;
     }
 
     toTag() {
-      return `${this.name},${this.axes},${this.category},${this.score}`;
+      const prov = this.providence ? `,${this.providence}` : "";
+      return `${this.name},${this.axes},${this.category},${this.score}${prov}`;
     }
     get tag() {
       return this.toTag();
@@ -812,7 +815,7 @@
           })
           .then(csvText => {
             this.tags = this.parseCSV(csvText);
-            csvText = "Family,Axes,Group/Tag,Weight\r\n" + csvText;
+            csvText = "Family,Axes,Group/Tag,Weight,Providence\r\n" + csvText;
         })
       },
       parseCSV(text) {

.ci/test_font_tags.py

@@ -24,7 +24,8 @@ def family_tags():
     for row in reader:
         if not row:
             continue
-        res.append([row[0], row[1], row[2], float(row[3])])
+        providence = row[4] if len(row) > 4 else ""
+        res.append([row[0], row[1], row[2], float(row[3]), providence])
     return res
 
 
@@ -59,15 +60,15 @@ def test_categories_exist(family_tags, tags_metadata):
     tags_metadata.csv file
     """
     meta_categories = set(tags_metadata)
-    families_categories = set(cat for _, _, cat, _ in family_tags)
+    families_categories = set(cat for _, _, cat, *_ in family_tags)
     missing = families_categories - meta_categories
     assert not missing, f"Missing categories: {missing}"
 
 
 def test_no_duplicate_families(family_tags):
     seen = set()
     dups = []
-    for family, axes, cat, _ in family_tags:
+    for family, axes, cat, *_ in family_tags:
         key = (family, axes, cat)
         if key in seen:
             dups.append(",".join(key))

tags/all/README.md

@@ -1,5 +1,5 @@
 Contents:
 
-* families.csv - expressive and typographic tags, human assessed
+* families.csv - expressive and typographic tags, human assessed and machine predicted.
 * quant.csv - measured values, machine assessed
 * skip.csv - signals to machine assessors, formulated as tags, human assessed