README.md

metadata

license: apache-2.0
datasets:
  - Skywork/SkyPile-150B
  - ticoAg/shibing624-medical-pretrain
  - togethercomputer/RedPajama-Data-V2
  - medalpaca/medical_meadow_wikidoc
  - nlp-guild/medical-data
language:
  - en
  - zh
pipeline_tag: text-classification

fasttext-med-en-zh-identification

[中文] [English]

This model is an intermediate result of the EPCD (Easy-Data-Clean-Pipeline) project. It is primarily designed to accurately distinguish between Chinese and English samples in medical pretraining datasets. The model framework uses fastText.

Data Composition

General Chinese Pretraining Dataset

• Skywork/SkyPile-150B

Medical Chinese Pretraining Dataset

• ticoAg/shibing624-medical-pretrain

General English Pretraining Dataset

• togethercomputer/RedPajama-Data-V2

Medical English Pretraining Datasets

• medalpaca/medical_meadow_wikidoc
• nlp-guild/medical-data

The above datasets are high-quality, open-source datasets, which can save a lot of effort in data cleaning. Many thanks to the developers for their contributions to the open-source data community!

Data Cleaning Process

• Initial dataset processing:

  • For the Chinese training datasets, the pretraining corpus is split by \n, and any leading or trailing spaces are removed.
  • For the English training datasets, the pretraining corpus is split by \n, all letters are converted to lowercase, and any leading or trailing spaces are removed.

• Word count statistics:

  • For Chinese, the jieba package is used for tokenization, and stopwords and non-Chinese characters are further filtered using jionlp.
  • For English, the nltk package is used for tokenization, with built-in stopwords for filtering.

• Sample filtering based on word count (heuristic thresholds):

  • For Chinese: Keep only samples with more than 5 words.
  • For English: Keep only samples with more than 5 words.

• Dataset splitting: 90% of the data is used for training and 10% for testing.

Model Performance

Dataset	Precision	Recall
Train	0.9987	0.9987
Test	0.9962	0.9962

Usage Example

import fasttext
from huggingface_hub import hf_hub_download

def to_low(text):
    return text.strip().lower()

model_path = hf_hub_download(repo_id="ytzfhqs/fasttext-med-en-zh-identification", filename="model.bin")
model = fasttext.load_model(model_path)
model.predict(to_low('Hello, world!'))

fasttext-med-en-zh-identification

[中文] [English]

该模型为EPCD(Easy-Data-Clean-Pipeline)项目的中间产物，主要用来区分医疗预训练语料中中文与英文样本。模型框架使用fastText。

数据组成

中文通用预训练数据集

• Skywork/SkyPile-150B

中文医疗预训练数据集

• ticoAg/shibing624-medical-pretrain

英文通用预训练数据集

• togethercomputer/RedPajama-Data-V2

英文医疗预训练数据集

• medalpaca/medical_meadow_wikidoc
• nlp-guild/medical-data

上述数据集均为高质量开源数据集，可以节省很多数据清洗的工作，感谢上述开发者对开源数据社区的支持！

数据清洗流程

• 数据集初步整理
  • 对中文训练数据集，按\n分割预训练语料，去除开头和结尾可能存在的空格。
  • 对英文训练数据集，按\n分割预训练语料，将所有字母全部变为小写，去除开头和结尾可能存在的空格。
• 统计词数量，具体的：
  • 对中文，使用jieba包进行分词，并利用jionlp进一步过滤停用词和非中文字符。
  • 对英文，使用nltk包进行分词，并利用内置停用词进行过滤。
• 根据词数量进行样本过滤，具体的（经验数值）：
  • 对中文：仅保留词数量大于5的样本。
  • 对英文：仅保留词数量大于5的样本。
• 切分数据集，训练集比例为0.9，测试集比例为0.1。

模型表现

Dataset	Accuracy
Train	0.9994
Test	0.9998

Usage Example

import fasttext
from huggingface_hub import hf_hub_download

def to_low(text):
    return text.strip().lower()

model_path = hf_hub_download(repo_id="ytzfhqs/fasttext-med-en-zh-identification", filename="model.bin")
model = fasttext.load_model(model_path)
model.predict(to_low('Hello, world!'))