docs: update

README.md

@@ -1,3 +1,159 @@
+[中文版](#中文版)
+
+Cemotion is a Chinese NLP (Natural Language Processing) library for Python that can perform sentiment analysis and general domain Chinese word segmentation.
+
+The Cemotion 2.0 model is trained using BERT (Bidirectional Encoder Representations from Transformers), which returns a sentiment confidence score between 0 and 1 for Chinese text (sentiment polarity with 0 being negative and 1 being positive).
+
+Additionally, the newly added Cegementor Chinese word segmentation class uses the BAStructBERT general domain Chinese word segmentation model to segment text semantically.
+
+With Cemotion, you will be able to:
+
+- Analyze the sentiment of Chinese text in batches
+- Perform Chinese text segmentation in batches
+- Deploy to production environments such as Linux, macOS, Windows (supports Apple Silicon)
+
+This module depends on the PyTorch environment (which will be installed automatically) and requires Python version 3.8 or higher. Older machines may not be able to run it.
+
+Please note that Cemotion will automatically utilize the GPUs of NVIDIA and Apple Silicon. If there is no GPU available, it will use CPU for inference.
+
+### Installation Method
+
+1. Enter the command window and create a virtual environment by entering the following commands in sequence.
+
+For Linux and macOS:
+
+```bash
+python3 -m venv venv # Create a virtual environment
+. venv/bin/activate # Activate the virtual environment
+```
+
+For Windows:
+
+```bash
+python -m venv venv # Create a virtual environment
+venv\Scripts\activate # Activate the virtual environment
+```
+
+2. Install the Cemotion library by entering the following commands in sequence:
+
+```bash
+pip install --upgrade pip
+pip install cemotion
+```
+
+### Links
+
+- GitHub: https://github.com/Cyberbolt/Cemotion
+- Cyberlight Notes: https://www.cyberlight.xyz/
+
+### Usage
+
+#### Chinese Sentiment Classification
+
+```python
+# Analyze by text string
+from cemotion import Cemotion
+
+str_text1 = '配置顶级，不解释，手机需要的各个方面都很完美'
+str_text2 = '院线看电影这么多年以来，这是我第一次看电影睡着了。简直是史上最大烂片！没有之一！侮辱智商！大家小心警惕！千万不要上当！再也不要看了！'
+
+c = Cemotion()
+print(f'"{str_text1}"\n预测值:{c.predict(str_text1)}', '\n')
+print(f'"{str_text2}"\n预测值:{c.predict(str_text2)}' , '\n')
+```
+
+
+```
+# Return Content (This module returns the sentiment confidence score for the sentence, with a value ranging from 0 to 1):
+" 配置顶级，不解释，手机需要的各个方面都很完美 "
+ 预测值:0.999962 
+
+" 院线看电影这么多年以来，这是我第一次看电影睡着了。简直是史上最大烂片！没有之一！侮辱智商！大家小心警惕！千万不要上当！再也不要看了！ "
+ 预测值:0.000147
+```
+
+
+
+
+```python
+# Using a list for batch analysis
+from cemotion import Cemotion
+
+list_text = ['内饰蛮年轻的，而且看上去质感都蛮好，貌似本田所有车都有点相似，满高档的！',
+'总而言之，是一家不会再去的店。']
+
+c = Cemotion()
+print(c.predict(list_text))
+```
+
+
+```
+# Return Content (This module returns the sentiment confidence score for the sentence, with a value ranging from 0 to 1).:
+[['内饰蛮年轻的，而且看上去质感都蛮好，貌似本田所有车都有点相似，满高档的！', 0.999962], ['总而言之，是一家不会再去的店。', 0.000194]]
+```
+
+
+
+#### Chinese text segmentation
+
+```python
+# Segmentation of a single text
+from cemotion import Cegmentor
+
+text = '这辆车的内饰设计非常现代，而且用料考究，给人一种豪华的感觉。'
+
+model_id = 'damo/nlp_structbert_word-segmentation_chinese-base'
+segmenter = Cegmentor(model_id)
+segmentation_result = segmenter.segment(text)
+print(segmentation_result)
+```
+
+```
+# Return content as a single sentence segmentation
+['这', '辆', '车', '的', '内饰', '设计', '非常', '现代', '，', '而且', '用料', '考究', '，', '给', '人', '一', '种', '豪华', '的', '感觉', '。']
+```
+
+
+
+```python
+# Using a list for batch Chinese text segmentation
+from cemotion import Cegmentor
+
+text = '这辆车的内饰设计非常现代，而且用料考究，给人一种豪华的感觉。'
+
+list_text = [
+    '随着科技的发展，智能手机的功能越来越强大，给我们的生活带来了很多便利。',
+    '他从小就对天文学充满好奇，立志要成为一名宇航员，探索宇宙的奥秘。',
+    '这种新型的太阳能电池板转换效率高，而且环保，有望在未来得到广泛应用。'
+]
+model_id = 'damo/nlp_structbert_word-segmentation_chinese-base'
+segmenter = Cegmentor(model_id)
+segmentation_result = segmenter.segment(list_text)
+print(segmentation_result)
+```
+
+```
+# Return content as a list of segmentations
+[
+['随着', '科技', '的', '发展', '，', '智能', '手机', '的', '功能', '越来越', '强大', '，', '给', '我们', '的', '生活', '带来', '了', '很多', '便利', '。'],
+['他', '从小', '就', '对', '天文学', '充满', '好奇', '，', '立志', '要', '成为', '一', '名', '宇航员', '，', '探索', '宇宙', '的', '奥秘', '。'],
+['这种', '新型', '的', '太阳能', '电池板', '转换', '效率', '高', '，', '而且', '环保', '，', '有望', '在', '未来', '得到', '广泛', '应用', '。']
+]
+```
+
+
+
+### Main Updates in Version 2.0
+
+1. Replaced the dependency on TensorFlow with PyTorch.
+
+2. Changed the old version's BRNN + LSTM to the BERT model.
+
+Additionally, the interface of version 2.0 remains the same as the old version, allowing for seamless switching.
+
+
+# 中文版
+
 Cemotion 是 Python 下的中文 NLP 库，可以进行中文情感倾向分析、通用领域中文分词。
 
 Cemotion 2.0 模型使用 BERT (Bidirectional Encoder Representations from Transformers) 训练得到，会为中文文本返回 0～1 之间的情感倾向置信度 (情感极性 0 为消极，1 为积极)。
@@ -47,16 +203,17 @@ pip install cemotion
 
 #### 中文情感分类
 
+使用列表进行批量分析
+
 ```python
-#按文本字符串分析
 from cemotion import Cemotion
 
 str_text1 = '配置顶级，不解释，手机需要的各个方面都很完美'
 str_text2 = '院线看电影这么多年以来，这是我第一次看电影睡着了。简直是史上最大烂片！没有之一！侮辱智商！大家小心警惕！千万不要上当！再也不要看了！'
 
 c = Cemotion()
-print(f'"{str_text1}"\n预测值:{c.predict(str_text1)}', '\n')
-print(f'"{str_text2}"\n预测值:{c.predict(str_text2)}' , '\n')
+print(f'"{str_text1}"\n预测值:{c.predict(str_text1)}\n')
+print(f'"{str_text2}"\n预测值:{c.predict(str_text2)}\n' )
 ```
 
 
@@ -69,11 +226,9 @@ print(f'"{str_text2}"\n预测值:{c.predict(str_text2)}' , '\n')
  预测值:0.000147
 ```
 
-
-
+使用列表进行批量分析
 
 ```python
-#使用列表进行批量分析
 from cemotion import Cemotion
 
 list_text = ['内饰蛮年轻的，而且看上去质感都蛮好，貌似本田所有车都有点相似，满高档的！',
@@ -89,12 +244,11 @@ print(c.predict(list_text))
 [['内饰蛮年轻的，而且看上去质感都蛮好，貌似本田所有车都有点相似，满高档的！', 0.999962], ['总而言之，是一家不会再去的店。', 0.000194]]
 ```
 
-
-
 #### 中文文本分词
 
+单个文本进行分词
+
 ```python
-#单个文本进行分词
 from cemotion import Cegmentor
 
 text = '这辆车的内饰设计非常现代，而且用料考究，给人一种豪华的感觉。'
@@ -110,10 +264,9 @@ print(segmentation_result)
 ['这', '辆', '车', '的', '内饰', '设计', '非常', '现代', '，', '而且', '用料', '考究', '，', '给', '人', '一', '种', '豪华', '的', '感觉', '。']
 ```
 
-
+使用列表进行批量中文分词
 
 ```python
-#使用列表进行批量中文分词
 from cemotion import Cegmentor
 
 text = '这辆车的内饰设计非常现代，而且用料考究，给人一种豪华的感觉。'