Modified README.md and mkdocs to correct errors in grammer and spelling

README.md

@@ -1,5 +1,5 @@
 <h1 align="center">
-    Kashgari
+    <a href='https://en.wikipedia.org/wiki/Mahmud_al-Kashgari'>Kashgari</a>
 </h1>
 
 <p align="center">
@@ -29,21 +29,21 @@
     <a href="https://kashgari.bmio.net/about/contributing/">Contributing</a>
 </h4>
 
-🎉🎉🎉 We are proud to announce that we entirely rewrite  Kashgari with tf.keras, now Kashgari comes with cleaner API and faster speed. 🎉🎉🎉
+🎉🎉🎉 We are proud to announce that we entirely rewritten Kashgari with tf.keras, now Kashgari comes with easier to understand API and is faster! 🎉🎉🎉
 
 ## Overview
 
-Kashgari is simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks.
+Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks.
 
 - **Human-friendly**. Kashgari's code is straightforward, well documented and tested, which makes it very easy to understand and modify.
 - **Powerful and simple**. Kashgari allows you to apply state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS) and classification.
-- **Buildin transfer learning**. Kashgari build-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.
+- **Built-in transfer learning**. Kashgari built-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.
 - **Fully scalable**. Kashgari provide a simple, fast, and scalable environment for fast experimentation, train your models and experiment with new approaches using different embeddings and model structure. 
-- **Product Ready**. Kashgari could export model with `SavedModel` format for tensorflow serving, you could directly deploy it on cloud. 
+- **Production Ready**. Kashgari could export model with `SavedModel` format for tensorflow serving, you could directly deploy it on cloud. 
 
 ## Our Goal
 
-- **Academic users** Experiments to prove their hypothesis without coding from scratch.
+- **Academic users** Easier Experimentation to prove their hypothesis without coding from scratch.
 - **NLP beginners** Learn how to build an NLP project with production level code quality.
 - **NLP developers** Build a production level classification/labeling model within minutes.
 

mkdocs/docs/about/contributing.md

@@ -1,6 +1,6 @@
 # Contributing & Support
 
-We are happy to accept your contributions to make `Kashgari` better and more awesome! You could contribute in various ways:
+We are happy to accept contributions that make `Kashgari` better and more awesome! You could contribute in various ways:
 
 ## Bug Reports
 
@@ -25,7 +25,7 @@ Take part in reviewing pull requests and/or reviewing direct commits.  Make sugg
 
 ## Answer Questions in Issues
 
-Take time and answer questions and offer suggestions to people who've created issues in the issue tracker. Often people will have questions that you might have an answer for.  Or maybe you know how to help them accomplish a specific task they are asking about. Feel free to share your experience to help others out.
+Take time and answer questions and offer suggestions to people who've created issues in the issue tracker. Often people will have questions that you might have an answer for.  Or maybe you know how to help them accomplish a specific task they are asking about. Feel free to share your experience with others to help them out.
 
 ## Pull Requests
 

mkdocs/docs/advance-use/handle-numeric-features.md

@@ -4,8 +4,8 @@
 
 https://github.com/BrikerMan/Kashgari/issues/90
 
-Some time, except the text, we have some additional features like text formatting (italic, bold, centered), 
-position in text and more. Kashgari provides `NumericFeaturesEmbedding` and `StackedEmbedding` for this kine data. Here is the details.
+At times there have some additional features like text formatting (italic, bold, centered), 
+position in text and more. Kashgari provides `NumericFeaturesEmbedding` and `StackedEmbedding` for this kind data. Here is the details:
 
 If you have a dataset like this.
 
@@ -51,7 +51,7 @@ label_list = [label] * 100
 
 SEQUENCE_LEN = 100
 
-# You can use WordEmbedding or BERTEmbedding for your text embedding
+# You can use Word Embedding or BERT Embedding for your text embedding
 text_embedding = BareEmbedding(task=kashgari.LABELING, sequence_length=SEQUENCE_LEN)
 start_of_p_embedding = NumericFeaturesEmbedding(feature_count=2,
                                                 feature_name='start_of_p',
@@ -65,7 +65,7 @@ center_embedding = NumericFeaturesEmbedding(feature_count=2,
                                                 feature_name='center',
                                                 sequence_length=SEQUENCE_LEN)
 
-# first one must be the text embedding
+# first attribute, must be the text embedding
 stack_embedding = StackedEmbedding([
     text_embedding,
     start_of_p_embedding,
@@ -77,11 +77,11 @@ x = (text_list, start_of_p_list, bold_list, center_list)
 y = label_list
 stack_embedding.analyze_corpus(x, y)
 
-# Now we can embed with this stacked embedding layer
+# Now we can embed using this stacked embedding layer
 print(stack_embedding.embed(x))
 ```
 
-Once embedding layer prepared, you could use all of the classification and labeling models.
+Once the embedding layer prepared, you can use all of the classification and labeling models.
 
 ```python
 # We can build any labeling model with this embedding
@@ -94,6 +94,6 @@ print(model.predict(x))
 print(model.predict_entities(x))
 ```
 
-This is the struct of this model.
+This is the structurer of this model.
 
 ![](../static/images/multi_feature_model.png)

mkdocs/docs/advance-use/multi-output-model.md

@@ -28,7 +28,7 @@ output_2 = [
     [0. 0. 1.]]
 ```
 
-Then you need to create a customized processor inhered from the `ClassificationProcessor`.
+Then you need to create a customized processor inherited from the `ClassificationProcessor`.
 
 ```python
 import kashgari
@@ -57,7 +57,7 @@ class MultiOutputProcessor(ClassificationProcessor):
             return tuple(result)
 ```
 
-Then build your own model inhered from the `BaseClassificationModel`
+Then build your own model inherited from the `BaseClassificationModel`
 
 ```python
 import kashgari

mkdocs/docs/api/tasks.labeling.md

@@ -84,9 +84,9 @@ def build_model(self,
 
 __Args__:
 
-- **x_train**: Array of train feature data (if the model has a single input),
-      or tuple of train feature data array (if the model has multiple inputs)
-- **y_train**: Array of train label data
+- **x_train**: Array of training feature data (if the model has a single input),
+      or tuple of training feature data array (if the model has multiple inputs)
+- **y_train**: Array of training label data
 - **x_validate**: Array of validation feature data (if the model has a single input),
       or tuple of validation feature data array (if the model has multiple inputs)
 - **y_validate**: Array of validation label data
@@ -114,9 +114,9 @@ __Args__:
 - **cpu_relocation**: A boolean value to identify whether to create the model's weights
     under the scope of the CPU. If the model is not defined under any preceding device
     scope, you can still rescue it by activating this option.
-- **x_train**: Array of train feature data (if the model has a single input),
-    or tuple of train feature data array (if the model has multiple inputs)
-- **y_train**: Array of train label data
+- **x_train**: Array of training feature data (if the model has a single input),
+    or tuple of training feature data array (if the model has multiple inputs)
+- **y_train**: Array of training label data
 - **x_validate**: Array of validation feature data (if the model has a single input),
     or tuple of validation feature data array (if the model has multiple inputs)
 - **y_validate**: Array of validation label data
@@ -137,9 +137,9 @@ __Args__:
 
 - **strategy**: `TPUDistributionStrategy`. The strategy to use for replicating model
     across multiple TPU cores.
-- **x_train**: Array of train feature data (if the model has a single input),
-    or tuple of train feature data array (if the model has multiple inputs)
-- **y_train**: Array of train label data
+- **x_train**: Array of training feature data (if the model has a single input),
+    or tuple of training feature data array (if the model has multiple inputs)
+- **y_train**: Array of training label data
 - **x_validate**: Array of validation feature data (if the model has a single input),
     or tuple of validation feature data array (if the model has multiple inputs)
 - **y_validate**: Array of validation label data
@@ -207,9 +207,9 @@ def fit(self,
 
 __Args__:
 
-- **x_train**: Array of train feature data (if the model has a single input),
-    or tuple of train feature data array (if the model has multiple inputs)
-- **y_train**: Array of train label data
+- **x_train**: Array of training feature data (if the model has a single input),
+    or tuple of training feature data array (if the model has multiple inputs)
+- **y_train**: Array of training label data
 - **x_validate**: Array of validation feature data (if the model has a single input),
     or tuple of validation feature data array (if the model has multiple inputs)
 - **y_validate**: Array of validation label data
@@ -241,9 +241,9 @@ def fit_without_generator(self,
 
 __Args__:
 
-- **x_train**: Array of train feature data (if the model has a single input),
-    or tuple of train feature data array (if the model has multiple inputs)
-- **y_train**: Array of train label data
+- **x_train**: Array of training feature data (if the model has a single input),
+    or tuple of training feature data array (if the model has multiple inputs)
+- **y_train**: Array of training label data
 - **x_validate**: Array of validation feature data (if the model has a single input),
     or tuple of validation feature data array (if the model has multiple inputs)
 - **y_validate**: Array of validation label data

mkdocs/docs/index.md

@@ -1,5 +1,5 @@
 <h1 align="center" >
-    <strong style="color: rgba(0,0,0,.87);">Kashgari</strong>
+    <a href='https://en.wikipedia.org/wiki/Mahmud_al-Kashgari'><strong style="color: rgba(0,0,0,.87);">Kashgari</strong></a>
 </h1>
 
 <p align="center">
@@ -20,34 +20,37 @@
     </a>
 </p>
 
-🎉🎉🎉 We are proud to announce that we entirely rewrite  Kashgari with tf.keras, now Kashgari comes with cleaner API and faster speed. 🎉🎉🎉
+🎉🎉🎉 We are proud to announce that we entirely rewritten Kashgari with tf.keras, now Kashgari comes with easier to understand API and is faster! 🎉🎉🎉
 
-Kashgari is simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS) and text classification tasks.
+## Overview
+
+Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks.
 
 - **Human-friendly**. Kashgari's code is straightforward, well documented and tested, which makes it very easy to understand and modify.
 - **Powerful and simple**. Kashgari allows you to apply state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS) and classification.
 - **Built-in transfer learning**. Kashgari built-in pre-trained BERT and Word2vec embedding models, which makes it very simple to transfer learning to train your model.
 - **Fully scalable**. Kashgari provide a simple, fast, and scalable environment for fast experimentation, train your models and experiment with new approaches using different embeddings and model structure. 
-- **Product Ready**. Kashgari could export model with `SavedModel` format for tensorflow serving, you could directly deploy it on cloud.
+- **Production Ready**. Kashgari could export model with `SavedModel` format for tensorflow serving, you could directly deploy it on cloud. 
 
 ## Our Goal
 
-- **Academic users** Experiments to prove their hypothesis without coding from scratch.
+- **Academic users** Easier Experimentation to prove their hypothesis without coding from scratch.
 - **NLP beginners** Learn how to build an NLP project with production level code quality.
 - **NLP developers** Build a production level classification/labeling model within minutes.
 
 ## Performance
 
-| Task                     | Language | Dataset                   | Score          | Detail                                                                             |
-| ------------------------ | -------- | ------------------------- | -------------- | ---------------------------------------------------------------------------------- |
-| Named Entity Recognition | Chinese  | People's Daily Ner Corpus | **94.46** (F1) | [Text Labeling Performance Report](./tutorial/text-labeling.md#performance-report) |
+| Task                     | Language | Dataset                   | Score          | Detail                                                                                                             |
+| ------------------------ | -------- | ------------------------- | -------------- | ------------------------------------------------------------------------------------------------------------------ |
+| Named Entity Recognition | Chinese  | People's Daily Ner Corpus | **94.46** (F1) | [Text Labeling Performance Report](https://kashgari.bmio.net/tutorial/text-labeling/#performance-report) |
 
 ## Tutorials
 
 Here is a set of quick tutorials to get you started with the library:
 
-- [Tutorial 1: Text Classification Model](tutorial/text-classification.md)
-- [Tutorial 2: Text Labeling Model](tutorial/text-labeling.md)
+- [Tutorial 1: Text Classification](https://kashgari.bmio.net/tutorial/text-classification/)
+- [Tutorial 2: Text Labeling](https://kashgari.bmio.net/tutorial/text-labeling/)
+- [Tutorial 3: Language Embedding](https://kashgari.bmio.net/embeddings/)
 
 There are also articles and posts that illustrate how to use Kashgari:
 
@@ -61,8 +64,7 @@ There are also articles and posts that illustrate how to use Kashgari:
 
 ### Requirements and Installation
 
-!!!important
-    We renamed the tf.keras version as `kashgari-tf`
+🎉🎉🎉 We renamed the tf.keras version as **kashgari-tf** 🎉🎉🎉
 
 The project is based on TenorFlow 1.14.0 and Python 3.6+, because it is 2019 and type hints is cool.
 
@@ -74,7 +76,7 @@ pip install tensorflow==1.14.0
 pip install tensorflow-gpu==1.14.0
 ```
 
-### Basic Usage
+### Example Usage
 
 lets run a NER labeling model with Bi_LSTM Model.
 
@@ -147,7 +149,7 @@ model.fit(train_x, train_y)
 
 ## Contributing
 
-Thanks for your interest in contributing! There are many ways to get involved; start with the [contributor guidelines](about/contributing.md) and then check these open issues for specific tasks.
+Thanks for your interest in contributing! There are many ways to get involved; start with the [contributor guidelines](https://kashgari.bmio.net/about/contributing/) and then check these open issues for specific tasks.
 
 ## Reference
 

mkdocs/docs/tutorial/text-classification.md

@@ -170,8 +170,7 @@ model.fit(x, y)
 ## Customize your own model
 
 It is very easy and straightforward to build your own customized model,
-just inherit the `BaseClassificationModel` and implement the `get_default_hyper_parameters()` function
-and `build_model_arc()` function.
+just inherit the `BaseClassificationModel` and implement the `get_default_hyper_parameters()` function and `build_model_arc()` function.
 
 ```python
 from typing import Dict, Any