Upgrading to TeNs 2.x

website/meta/universe.json

@@ -3,7 +3,7 @@
         {
             "id": "TeNs",
             "title": "Temporal Expressions Normalization spaCy",
-            "thumb": "https://github-production-user-asset-6210df.s3.amazonaws.com/40547052/433595900-fae3c9d9-7181-4d8b-8b49-e6dc4fca930b.png?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20250414%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250414T235545Z&X-Amz-Expires=300&X-Amz-Signature=e21d3c06300ceb15fa1dadd7cb60081cc9f1b35e5a7bfd07f6e8b90dd7fad9d0&X-Amz-SignedHeaders=host",
+            "thumb": "https://raw.githubusercontent.com/iliedorobat/timespan-normalization-spacy/refs/heads/main/icon.png",
             "url": "https://pypi.org/project/temporal-normalization-spacy/",
             "slogan": "A temporal expression normalization plugin for Romanian using rule-based methods and DBpedia mappings.",
             "description": "**[Temporal Expressions Normalization spaCy (TeNs)](https://github.com/iliedorobat/timespan-normalization-spacy)** is a powerful pipeline component for spaCy that seamlessly identifies and parses date entities in text. It leverages the **[Temporal Expressions Normalization Framework]( https://github.com/iliedorobat/timespan-normalization)** to recognize a wide variety of date formats using an extensive set of regular expressions (RegEx), ensuring robust and adaptable date extraction across diverse textual sources.\n\nUnlike conventional solutions that primarily focus on well-structured date formats, TeNs excels in handling real-world text by **identifying** not only standard date representations but also **abbreviated, informal, or even misspelled temporal expressions.** This makes it particularly effective for processing noisy or unstructured data, such as historical records, user-generated content, and scanned documents with OCR inaccuracies.",
@@ -34,21 +34,25 @@
                 "",
                 "# Display information about the identified and normalized dates in the text.",
                 "for entity in doc.ents:",
-                "    edges = entity._.time_series.edges",
+                "    time_series = entity._.time_series",
                 "",
-                "    print('Start Edge:')",
-                "    print(edges.start.serialize('\\t'))",
-                "    print()",
+                "        if isinstance(time_series, list):",
+                "            for ts in time_series:",
+                "                edges = ts.edges",
                 "",
-                "    print('End Edge:')",
-                "    print(edges.end.serialize('\\t'))",
-                "    print()",
+                "                print('Start Edge:')",
+                "                print(edges.start.serialize('\\t'))",
+                "                print()",
                 "",
-                "    print('Periods:')",
-                "    for period in entity._.time_series.periods:",
-                "        print(period.serialize('\\t'))",
-                "        print()",
-                "    print('---------------------')"
+                "                print('End Edge:')",
+                "                print(edges.end.serialize('\\t'))",
+                "                print()",
+                "",
+                "                print('Periods:')",
+                "                for period in ts.periods:",
+                "                    print(period.serialize('\\t'))",
+                "                    print()",
+                "                print('---------------------')"
             ],
             "code_language": "python",
             "author": "Ilie Cristian Dorobat",