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---
license: mit
datasets:
- kenhktsui/code-natural-language-classification-dataset
language:
- en
metrics:
- f1
pipeline_tag: text-classification
library_name: fasttext
---
# code-natural-language-classification-dataset
[Dataset](https://huggingface.co/datasets/kenhktsui/code-natural-language-classification-dataset)
This classifier classifies a text into Code or NaturalLanguage.
The model is trained over 3.24M records, which is a mix of code and natural langauge and achieved a test F1 score of 0.97.
The classifier can be used for LLM pretraining data curation, to route a text into different pipeline (e.g. code syntax check).
It is ultra fast ⚡ with a throughtput of ~2000 doc/s with CPU.
## 🛠️Usage
```python
from typing import List
import re
from huggingface_hub import hf_hub_download
import fasttext
model_hf = fasttext.load_model(hf_hub_download("kenhktsui/code-natural-language-fasttext-classifier", "model.bin")) # "model_quantized.bin" for quantized version
def replace_newlines(text: str) -> str:
return re.sub("\n+", " ", text)
def predict(text_list: List[str]) -> List[dict]:
text_list = [replace_newlines(text) for text in text_list]
pred = model.predict(text_list)
return [{"label": l[0].lstrip("__label__"), "score": s[0]}
for l, s in zip(*pred)]
predict([
"""This is a lightning fast model, which can classify at throughtput of 2000 doc/s with CPU""",
"""import torch""",
"""Short text won't work"""
])
# [{'label': 'NaturalLanguage', 'score': 0.96747404},
# {'label': 'Code', 'score': 1.00001},
# {'label': 'Code', 'score': 1.000009}]
```
## 📊Evaluation
full version
```
precision recall f1-score support
Code 0.97 1.00 0.98 581282
NaturalLanguage 1.00 0.92 0.95 228993
accuracy 0.98 810275
macro avg 0.98 0.96 0.97 810275
weighted avg 0.98 0.98 0.98 810275
```
quantized version
```
precision recall f1-score support
Code 0.95 1.00 0.97 581282
NaturalLanguage 1.00 0.86 0.93 228993
micro avg 0.96 0.96 0.96 810275
macro avg 0.97 0.93 0.95 810275
weighted avg 0.96 0.96 0.96 810275
```
## 📝Definition of Label
Code covers:
```
{'Assembly',
'Batchfile',
'C',
'C#',
'C++',
'CMake',
'CSS',
'Dockerfile',
'FORTRAN',
'GO',
'HTML',
'Haskell',
'Java',
'JavaScript',
'Julia',
'Lua',
'Makefile',
'PHP',
'Perl',
'PowerShell',
'Python',
'Ruby',
'Rust',
'SQL',
'Scala',
'Shell',
'TeX',
'TypeScript',
'Visual Basic'}
```
Markdown is disregarded as it has a high overlap with natural language.
## ⚠️Known Limitation
The classifier does not handle short text well, which might not be surprising.
It has a tendency to classify short natural language into code, which you might find so in code comment.
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