Fix markdownlint

.markdownlint.json

@@ -2,7 +2,8 @@
   "default": true,
   "MD013": {
     "line_length": 150,
-    "tables": false
+    "tables": false,
+    "code_blocks": false
   },
   "MD033": false,
   "MD041": false,

README.md

@@ -104,7 +104,7 @@ please cite the software as follows:
 
 > Phatthiyaphaibun, Wannaphong, Korakot Chaovavanich, Charin Polpanumas,
 > Arthit Suriyawongkul, Lalita Lowphansirikul, and Pattarawat Chormai.
-> “Pythainlp: Thai Natural Language Processing in Python”.
+> “PyThaiNLP: Thai Natural Language Processing in Python”.
 > Zenodo, 2 June 2024. <http://doi.org/10.5281/zenodo.3519354>.
 
 with this BibTeX entry:

README_TH.md

@@ -131,7 +131,10 @@ PyThaiNLP ดาวน์โหลดข้อมูล (ดูแค็ตต
 
 หากคุณใช้ซอฟต์แวร์ `PyThaiNLP` ในโครงงานหรืองานวิจัยของคุณ คุณสามารถอ้างอิงได้ตามนี้:
 
-> Phatthiyaphaibun, Wannaphong, Korakot Chaovavanich, Charin Polpanumas, Arthit Suriyawongkul, Lalita Lowphansirikul, and Pattarawat Chormai. “Pythainlp: Thai Natural Language Processing in Python”. Zenodo, 2 June 2024. <http://doi.org/10.5281/zenodo.3519354>.
+> Phatthiyaphaibun, Wannaphong, Korakot Chaovavanich, Charin Polpanumas,
+> Arthit Suriyawongkul, Lalita Lowphansirikul, and Pattarawat Chormai.
+> “PyThaiNLP: Thai Natural Language Processing in Python”.
+> Zenodo, 2 June 2024. <http://doi.org/10.5281/zenodo.3519354>.
 
 โดยใช้รายการ BibTeX นี้:
 
@@ -157,7 +160,15 @@ PyThaiNLP ดาวน์โหลดข้อมูล (ดูแค็ตต
 [NLP-OSS 2023](https://nlposs.github.io/2023/)
 คุณสามารถอ้างอิงได้ตามนี้:
 
-> Wannaphong Phatthiyaphaibun, Korakot Chaovavanich, Charin Polpanumas, Arthit Suriyawongkul, Lalita Lowphansirikul, Pattarawat Chormai, Peerat Limkonchotiwat, Thanathip Suntorntip, and Can Udomcharoenchaikit. 2023. [PyThaiNLP: Thai Natural Language Processing in Python.](https://aclanthology.org/2023.nlposs-1.4) In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 25–36, Singapore, Singapore. Empirical Methods in Natural Language Processing.
+> Wannaphong Phatthiyaphaibun, Korakot Chaovavanich, Charin Polpanumas,
+> Arthit Suriyawongkul, Lalita Lowphansirikul, Pattarawat Chormai,
+> Peerat Limkonchotiwat, Thanathip Suntorntip, and Can Udomcharoenchaikit.
+> 2023.
+> [PyThaiNLP: Thai Natural Language Processing in Python.](https://aclanthology.org/2023.nlposs-1.4)
+> In Proceedings of the 3rd Workshop for Natural Language Processing
+> Open Source Software (NLP-OSS 2023),
+> pages 25–36, Singapore, Singapore.
+> Empirical Methods in Natural Language Processing.
 
 โดยใช้รายการ BibTeX นี้:
 

pythainlp/corpus/corpus_license.md

@@ -1,8 +1,16 @@
 # Corpus License
 
-- Corpora, datasets, and documentation created by the PyThaiNLP project are released under [Creative Commons Zero 1.0 Universal Public Domain Dedication License](https://creativecommons.org/publicdomain/zero/1.0/) (CC0).
-- Language models created by the PyThaiNLP project are released under [Creative Commons Attribution 4.0 International Public License](https://creativecommons.org/licenses/by/4.0/) (CC-by).
-- For more information about corpora that PyThaiNLP uses, see [https://github.com/PyThaiNLP/pythainlp-corpus/](https://github.com/PyThaiNLP/pythainlp-corpus/).
+- Corpora, datasets, and documentation created by the PyThaiNLP project are
+  released under
+  [Creative Commons Zero 1.0 Universal Public Domain Dedication License][cc0]
+  (CC0-1.0).
+- Language models created by the PyThaiNLP project are released under
+  [Creative Commons Attribution 4.0 International Public License][cc-by] (CC-BY-4.0).
+- For more information about corpora that PyThaiNLP uses, see
+  <https://github.com/PyThaiNLP/pythainlp-corpus/>.
+
+[cc0]: https://creativecommons.org/publicdomain/zero/1.0/
+[cc-by]: https://creativecommons.org/licenses/by/4.0/
 
 ## Dictionaries and Word Lists
 

tests/README.md

@@ -1,3 +1,4 @@
+---
 SPDX-FileCopyrightText: 2026 PyThaiNLP Project
 SPDX-FileType: DOCUMENTATION
 SPDX-License-Identifier: Apache-2.0
@@ -88,11 +89,13 @@ on their dependency requirements.
 
 Different ML/AI frameworks often have conflicting version requirements for
 their dependencies. For example:
+
 - PyTorch and TensorFlow may require different versions of numpy or protobuf
 - Large frameworks take significant time to install (~1-3 GB each)
 - Some packages require Cython compilation or system libraries
 
 By separating tests by dependency group, we can:
+
 - Test each framework independently without conflicts
 - Optimize CI/CD resources by running only relevant test groups
 - Make it easier for developers to test specific functionality
@@ -106,9 +109,9 @@ By separating tests by dependency group, we can:
 - Use this for comprehensive testing when all dependencies are available
 - Test case class suffix: `TestCaseN`
 
-#### Modular suites by dependency:
+#### Modular suites by dependency
 
-**PyTorch-based: tests.noauto_torch**
+##### PyTorch-based: tests.noauto_torch
 
 - Run `unittest tests.noauto_torch`
   - Need dependencies from `pip install "pythainlp[noauto-torch]"`
@@ -121,7 +124,7 @@ By separating tests by dependency group, we can:
 - Dependencies: ~2-3 GB
 - Test case class suffix: `TestCaseN`
 
-**TensorFlow-based: tests.noauto_tensorflow**
+##### TensorFlow-based: tests.noauto_tensorflow
 
 - Run `unittest tests.noauto_tensorflow`
   - Need dependencies from `pip install "pythainlp[noauto-tensorflow]"`
@@ -131,7 +134,7 @@ By separating tests by dependency group, we can:
 - Note: May conflict with PyTorch dependencies
 - Test case class suffix: `TestCaseN`
 
-**ONNX Runtime-based: tests.noauto_onnx**
+##### ONNX Runtime-based: tests.noauto_onnx
 
 - Run `unittest tests.noauto_onnx`
   - Need dependencies from `pip install "pythainlp[noauto-onnx]"`
@@ -140,7 +143,7 @@ By separating tests by dependency group, we can:
 - Dependencies: ~200-500 MB
 - Test case class suffix: `TestCaseN`
 
-**Cython-compiled: tests.noauto_cython**
+##### Cython-compiled: tests.noauto_cython
 
 - Run `unittest tests.noauto_cython`
   - Need dependencies from `pip install "pythainlp[noauto-cython]"`
@@ -150,12 +153,12 @@ By separating tests by dependency group, we can:
 - Platform-specific build requirements
 - Test case class suffix: `TestCaseN`
 
-**Network-dependent: tests.noauto_network**
+##### Network-dependent: tests.noauto_network
 
 - Run `unittest tests.noauto_network`
   - Need dependencies from `pip install "pythainlp[noauto-network]"`
 - Tests requiring network access:
-  - HuggingFace Hub model downloads
+  - Hugging Face Hub model downloads
   - External API calls
 - Requires: Internet connection, may involve large downloads
 - Test case class suffix: `TestCaseN`

tests/corpus/README.md

@@ -1,3 +1,4 @@
+---
 SPDX-FileCopyrightText: 2026 PyThaiNLP Project
 SPDX-FileType: DOCUMENTATION
 SPDX-License-Identifier: Apache-2.0
@@ -91,11 +92,11 @@ Each test verifies:
 1. **Loadability**: File can be loaded without errors
 2. **Type correctness**: Returns expected data type
    (frozenset, list, dict)
-4. **Non-empty**: Contains actual data
-5. **Format validity**: Data structure matches expected format
-6. **Content validity**: Contains expected content
+3. **Non-empty**: Contains actual data
+4. **Format validity**: Data structure matches expected format
+5. **Content validity**: Contains expected content
    (e.g., Thai characters)
-8. **Catalog functionality**: Catalog can be downloaded
+6. **Catalog functionality**: Catalog can be downloaded
    and queried correctly
 
 ## Adding new tests