ci: update actions to latest versions, bump Python to >=3.11

- actions/checkout v4 → v6
- actions/setup-python v5 → v6
- astral-sh/setup-uv v5 → v7
- actions/cache v4 → v5
- Drop Python 3.10 (onnxruntime 1.24.3 requires >=3.11)
- Add Python 3.14 to CI matrix and classifiers

Author: titusz

.github/workflows/tests.yml

@@ -10,17 +10,17 @@ jobs:
     strategy:
       fail-fast: true
       matrix:
-        python-version: ['3.10', '3.11', '3.12', '3.13']
+        python-version: ['3.11', '3.12', '3.13', '3.14']
         os: [ubuntu-latest, macos-latest, windows-latest]
     runs-on: ${{ matrix.os }}
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
+      - uses: actions/checkout@v6
+      - uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v7
         with:
           enable-cache: true
           cache-dependency-glob: "uv.lock"
@@ -48,7 +48,7 @@ jobs:
                   print(f"hash={config_hash}", file=fh)
 
       - name: Cache ONNX models
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ${{ steps.model-cache-dir.outputs.dir }}
           key: ${{ runner.os }}-onnx-models-${{ steps.model-cache-dir.outputs.hash }}

README.md

@@ -277,10 +277,10 @@ Text Input → Text Chunking → Embedding Generation → Vector Aggregation →
 `iscc-sct` employs the following process:
 
 1. Splits the text into overlaping chunks (using syntactically sensible breakpoints).
-2. Uses a pre-trained deep learning model for text embedding.
-3. Generates feature vectors capturing essential characteristics of the chunks.
-4. Aggregates these vectors and binarizes them to produce a Semantic Text-Code.
-5. Prefixes the binarized vector with the matching ISCC header, encodes it with base32, and adds the
+1. Uses a pre-trained deep learning model for text embedding.
+1. Generates feature vectors capturing essential characteristics of the chunks.
+1. Aggregates these vectors and binarizes them to produce a Semantic Text-Code.
+1. Prefixes the binarized vector with the matching ISCC header, encodes it with base32, and adds the
    "ISCC:" prefix.
 
 This process ensures robustness to variations and translations, enabling cross-lingual matching

pyproject.toml

@@ -7,7 +7,7 @@ authors = [
 ]
 license = {text = "Apache-2.0"}
 readme = "README.md"
-requires-python = ">=3.10,<=3.13"
+requires-python = ">=3.11"
 keywords = ["iscc", "text similarity", "cross lingual", "semantic similarity"]
 classifiers = [
     "Development Status :: 4 - Beta",
@@ -16,10 +16,10 @@ classifiers = [
     "License :: OSI Approved :: Apache Software License",
     "Natural Language :: English",
     "Operating System :: OS Independent",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
     "Topic :: Text Processing",
     "Topic :: Text Processing :: General",
     "Topic :: Text Processing :: Indexing",
@@ -69,8 +69,9 @@ dev = [
     "mdformat-gfm-alerts",
     "mdformat-frontmatter",
     "gradio",
-    "radon>=6.0.1",
-    "xenon>=0.9.3",
+    "radon",
+    "xenon",
+    "datasets",
 ]
 
 [project.urls]