---
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]](#chinese)    [[English]](#english)

<a id="english"></a>

This model is an intermediate result of the [EPCD (Easy-Data-Clean-Pipeline)](https://github.com/ytzfhqs/EDCP) project. It is primarily designed to accurately distinguish between Chinese and English samples in medical pretraining datasets. The model framework uses [fastText](https://github.com/facebookresearch/fastText).

## Data Composition

### General Chinese Pretraining Dataset
- [Skywork/SkyPile-150B](https://huggingface.co/datasets/Skywork/SkyPile-150B)

### Medical Chinese Pretraining Dataset
- [ticoAg/shibing624-medical-pretrain](https://huggingface.co/datasets/ticoAg/shibing624-medical-pretrain)

### General English Pretraining Dataset
- [togethercomputer/RedPajama-Data-V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)

### Medical English Pretraining Datasets
- [medalpaca/medical_meadow_wikidoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc)
- [nlp-guild/medical-data](https://huggingface.co/datasets/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](https://github.com/fxsjy/jieba) package is used for tokenization, and stopwords and non-Chinese characters are further filtered using [jionlp](https://github.com/dongrixinyu/JioNLP).
  - For English, the [nltk](https://github.com/nltk/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
```python
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]](#chinese)    [[English]](#english)

<a id="chinese"></a>

该模型为[EPCD(Easy-Data-Clean-Pipeline)](https://github.com/ytzfhqs/EDCP)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](https://github.com/facebookresearch/fastText)。

# 数据组成

## 中文通用预训练数据集
 - [Skywork/SkyPile-150B](https://huggingface.co/datasets/Skywork/SkyPile-150B)
## 中文医疗预训练数据集
 - [ticoAg/shibing624-medical-pretrain](https://huggingface.co/datasets/ticoAg/shibing624-medical-pretrain)

 ## 英文通用预训练数据集
  - [togethercomputer/RedPajama-Data-V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)
 ## 英文医疗预训练数据集
  - [medalpaca/medical_meadow_wikidoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc)
  - [nlp-guild/medical-data](https://huggingface.co/datasets/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](https://github.com/fxsjy/jieba)包进行分词，并利用[jionlp](https://github.com/dongrixinyu/JioNLP)进一步过滤停用词和非中文字符。
      - 对英文，使用[nltk](https://github.com/nltk/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
```python
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!'))
```