💡 Fix link errors in the docs.

docs/conf.py

@@ -139,7 +139,7 @@ def update_markdown_content(folder: str):
         with open(file, 'r') as original:
             content = original.read()
         with open(file, 'w') as new:
-            new_content = content.replace('.md)', '.html)')
+            new_content = content.replace('.md)', ')')
             new.write(new_content)
 
 
@@ -183,10 +183,11 @@ def setup(app):
     # Change readme to rst file, and include in Sphinx index
     with open(rst_readme, 'w') as f:
         md_content = open(original_readme, 'r').read()
-        md_content = md_content.replace('(./docs/', '(./')
+        md_content = md_content.replace('(./docs/', '(../')
         md_content = md_content.replace('(./examples/',
                                         '(https://github.com/BrikerMan/Kashgari/blob/v2-trunk/examples/')
-        md_content = md_content.replace('.md)', '.html)')
+        md_content = md_content.replace('index.md)', ')')
+        md_content = md_content.replace('.md)', ')')
         f.write(convert(md_content))
         print(f'Saved RST file to {rst_readme}')
 

docs/embeddings/bert-embedding.rst

@@ -6,7 +6,7 @@ Bert Embedding
 
 
 
-BertEmbedding is a simple wrapped class of `Transformer Embedding </embeddings/transformer-embedding.html>`_. If you need load other kind of transformer based language model, please use the `Transformer Embedding </embeddings/transformer-embedding.html>`_.
+BertEmbedding is a simple wrapped class of `Transformer Embedding <../transformer-embedding>`_. If you need load other kind of transformer based language model, please use the `Transformer Embedding <../transformer-embedding>`_.
 
 .. note::
     When using pre-trained embedding, remember to use same tokenize tool with the embedding model, this will allow to access the full power of the embedding

docs/embeddings/index.rst

@@ -21,7 +21,7 @@ Kashgari provides several embeddings for language representation. Embedding laye
 
 All embedding classes inherit from the ``Embedding`` class and implement the ``embed()`` to embed your input sequence and ``embed_model`` property which you need to build you own Model. By providing the ``embed()`` function and ``embed_model`` property, Kashgari hides the the complexity of different language embedding from users, all you need to care is which language embedding you need.
 
-You could check out the Embedding API here: `link <../api/embeddings>`_
+You could check out the Embedding API document `here <../apis/embeddings/>`_
 
 Quick start
 -----------

scripts/docs-lint.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+set -e
+
+docker run --rm -it -u $(id -u):$(id -g) -v  "$PWD/_site":/mnt linkchecker/linkchecker index.html

scripts/docs-live.sh

@@ -2,6 +2,6 @@
 
 echo "Build and Run API documents"
 
-sh scripts/docs.sh
+sh scripts/docs-generate.sh
 
 python3 -m http.server --directory _site