From 1e7e16a6d9765e5a1b23b16e36f4f437ffc65db1 Mon Sep 17 00:00:00 2001 From: Cyberbolt <735245473@qq.com> Date: Sun, 28 Apr 2024 19:36:17 +0800 Subject: [PATCH] docs: update --- README(ENG.ver).md | 150 -------------------------------------- README.md | 175 ++++++++++++++++++++++++++++++++++++++++++--- README_en_ver.md | 150 -------------------------------------- 3 files changed, 164 insertions(+), 311 deletions(-) delete mode 100644 README(ENG.ver).md delete mode 100644 README_en_ver.md diff --git a/README(ENG.ver).md b/README(ENG.ver).md deleted file mode 100644 index 4d74cd1..0000000 --- a/README(ENG.ver).md +++ /dev/null @@ -1,150 +0,0 @@ -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. diff --git a/README.md b/README.md index 036184a..95c938b 100644 --- a/README.md +++ b/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。' diff --git a/README_en_ver.md b/README_en_ver.md deleted file mode 100644 index 4d74cd1..0000000 --- a/README_en_ver.md +++ /dev/null @@ -1,150 +0,0 @@ -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.