How to match Chinese in Python

本文详细介绍了Python中re模块的search和match函数的使用方法及应用场景,通过实例演示如何在字符串中查找匹配模式。

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[\u4E00-\u9FFF]


Related Information:

python: re.search  and  re.match

### ET Framework Introduction The ET framework focuses on efficient and effective text encoding specifically designed for Chinese language models such as LLaMA and Alpaca[^1]. This framework aims to enhance the performance of these models by optimizing how textual information is processed and encoded. In terms of data preparation, for other versions of the Chinese LLAMA model, a general-purpose corpus sized at 20GB was utilized during pre-training. The content closely aligns with that used in training models like BERT-wwm, MacBERT, and LERT which also rely on comprehensive Chinese datasets[^2]. For advanced or “Plus” variations, an extended dataset reaching up to 120GB has been incorporated into the pre-training phase. These additional resources include materials from CommonCrawl and encyclopedias among others, significantly enriching the foundational knowledge base of the model. ### Usage Demonstration To effectively utilize this framework within projects involving natural language processing tasks tailored towards Mandarin texts: #### Installation Firstly, ensure installation of necessary libraries supporting operations related to deep learning frameworks compatible with transformer architectures. ```bash pip install transformers torch ``` #### Model Initialization Initialize the specific variant intended for use based upon requirements regarding depth and breadth of understanding required for target applications. ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("path_to_model") model = AutoModelForMaskedLM.from_pretrained("path_to_model") ``` #### Data Preprocessing Prepare input sequences adhering to guidelines established within documentation provided alongside chosen pretrained weights ensuring tokenization parameters match those applied during original training phases. ```python text = "这是一个测试句子" tokens = tokenizer(text, return_tensors="pt", max_length=512, truncation=True, padding='max_length') ``` #### Inference Execution Perform inference using prepared inputs passing through layers defined inside selected architecture ultimately yielding predictions reflecting learned patterns present across vast corpora ingested throughout development stages. ```python output = model(**tokens) predicted_ids = output.logits.argmax(dim=-1) decoded_text = tokenizer.decode(predicted_ids[0], skip_special_tokens=True) print(decoded_text) ```
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