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105 lines
4.0 KiB
105 lines
4.0 KiB
# Data PreProcessing for chinese Whole Word Masked
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<span id='all_catelogue'/>
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## Catalogue:
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* <a href='#introduction'>1. Introduction</a>
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* <a href='#Quick Start Guide'>2. Quick Start Guide:</a>
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* <a href='#Split Sentence'>2.1. Split Sentence</a>
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* <a href='#Tokenizer & Whole Word Masked'>2.2.Tokenizer & Whole Word Masked</a>
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<span id='introduction'/>
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## 1. Introduction: <a href='#all_catelogue'>[Back to Top]</a>
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This folder is used to preprocess chinese corpus with Whole Word Masked. You can obtain corpus from [WuDao](https://resource.wudaoai.cn/home?ind&name=WuDaoCorpora%202.0&id=1394901288847716352). Moreover, data preprocessing is flexible, and you can modify the code based on your needs, hardware or parallel framework(Open MPI, Spark, Dask).
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<span id='Quick Start Guide'/>
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## 2. Quick Start Guide: <a href='#all_catelogue'>[Back to Top]</a>
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<span id='Split Sentence'/>
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### 2.1. Split Sentence & Split data into multiple shard:
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Firstly, each file has multiple documents, and each document contains multiple sentences. Split sentence through punctuation, such as `。！`. **Secondly, split data into multiple shard based on server hardware (cpu, cpu memory, hard disk) and corpus size.** Each shard contains a part of corpus, and the model needs to train all the shards as one epoch.
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In this example, split 200G Corpus into 100 shard, and each shard is about 2G. The size of the shard is memory-dependent, taking into account the number of servers, the memory used by the tokenizer, and the memory used by the multi-process training to read the shard (n data parallel requires n\*shard_size memory). **To sum up, data preprocessing and model pretraining requires fighting with hardware, not just GPU.**
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```python
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python sentence_split.py --input_path /orginal_corpus --output_path /shard --shard 100
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# This step takes a short time
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```
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* `--input_path`: all original corpus, e.g., /orginal_corpus/0.json /orginal_corpus/1.json ...
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* `--output_path`: all shard with split sentences, e.g., /shard/0.txt, /shard/1.txt ...
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* `--shard`: Number of shard, e.g., 10, 50, or 100
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<summary><b>Input json:</b></summary>
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```
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[
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{
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"id": 0,
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"title": "打篮球",
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"content": "我今天去打篮球。不回来吃饭。"
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}
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{
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"id": 1,
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"title": "旅游",
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"content": "我后天去旅游。下周请假。"
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}
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]
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```
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<summary><b>Output txt:</b></summary>
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```
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我今天去打篮球。
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不回来吃饭。
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]]
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我后天去旅游。
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下周请假。
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```
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<span id='Tokenizer & Whole Word Masked'/>
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### 2.2. Tokenizer & Whole Word Masked:
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```python
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python tokenize_mask.py --input_path /shard --output_path /h5 --tokenizer_path /roberta --backend python
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# This step is time consuming and is mainly spent on mask
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```
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**[optional but recommended]**: the C++ backend with `pybind11` can provide faster speed
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```shell
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make
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```
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* `--input_path`: location of all shard with split sentences, e.g., /shard/0.txt, /shard/1.txt ...
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* `--output_path`: location of all h5 with token_id, input_mask, segment_ids and masked_lm_positions, e.g., /h5/0.h5, /h5/1.h5 ...
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* `--tokenizer_path`: tokenizer path contains huggingface tokenizer.json. Download config.json, special_tokens_map.json, vocab.txt and tokenzier.json from [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/tree/main)
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* `--backend`: python or c++, **specifies c++ can obtain faster preprocess speed**
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* `--dupe_factor`: specifies how many times the preprocessor repeats to create the input from the same article/document
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* `--worker`: number of process
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<summary><b>Input txt:</b></summary>
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```
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我今天去打篮球。
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不回来吃饭。
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]]
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我后天去旅游。
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下周请假。
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```
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<summary><b>Output h5+numpy:</b></summary>
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```
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'input_ids': [[id0,id1,id2,id3,id4,id5,id6,0,0..],
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...]
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'input_mask': [[1,1,1,1,1,1,0,0..],
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...]
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'segment_ids': [[0,0,0,0,0,...],
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...]
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'masked_lm_positions': [[label1,-1,-1,label2,-1...],
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...]
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``` |