semvec/pypi-packages huggingface dataset:
text_process 2.4.1
easy utils for process text


    # 环境：python3    
    # 安装：pip install text_process    
    # 使用示例：   
    
    from text_process import text_utils   
    text=""   
    # 中文分句：   
    text_utils.text2sencents_zh(text)    
    # 英文分句：   
    text_utils.text2sencents_eh(text)   
    # 英文分词（词组、单词、且已去停用词）    
    text_utils.text2sencents_eh(text)    
    # 中文分词（已去停用词）    
    text_utils.text_process_zh_single(text)    
    # 中文分词（词组 已去停用词）     
    text_utils.text_process_zh_not_single(text)     
    # 中文关键字(不包括词组)    
    text_utils.getKeywords_zh_single(text)    
    # 中文关键字(包括词组)     
    text_utils.getKeywords_zh_not_single(text)    
    # 英文关键字（包括词组）    
    text_utils.getKeywords_en(text)    
    # 文本相似度比较    
    compare_botor=text_utils.compare_bot()     
    text2=""     
    compare_bot.compare_two_txt_accuracy(text,text2)     
    #或者（适合少量数据）     
    text_utils.compare_two_txt_accuracy(text,text2)    
    # 中文摘要    
    text_utils.getAbstract_zh(title,text)     
    # 英文摘要     
    text_utils.getAbstract_en(title,text)
tfrecordutils 0.1.4
Utilities to simplify working with TFRecord files and TensorFlow's data pipeline API

# TFRecordUtils

Making life easier for working with TFRecord files and TensorFlow's data pipeline API.

## Installation

```
pip install tfrecordutils
```


### HOWTO
### Create a TFRecord file from a directory of images

* crops images by default to 500x500, but you can override this

```
jpg_glob     = './jpg/*.jpg'
tfrecord_file = 'images.tfrecords'

TFRecordUtils.jpgToTFRecord(jpg_glob)
```



### HOWTO
### Get an image batch iterator from a TFRecord file

```
    tfrecord_file = 'images.tfrecords'

    with tf.Session() as sess:

        iterator = TFRecordUtils.get_image_batch_iterator(sess, tfrecord_file)

        batch = sess.run(iterator)

        print(batch.shape)
```