---
license: mit
---
## Model Summary
This is a fastText-based binary classifier for identifying high-quality data in the pretraining corpus introduced in paper:  [Predictive Data Selection: The Data That Predicts Is the Data That Teaches
](). And this is also the classifier we used to build [PreSelect-100B](https://huggingface.co/datasets/hkust-nlp/PreSelect-100B) dataset with a selection threshold of 10%.
The positive label name and negative label name are "__label__1" and "__label__0" respectively.

## How to use
You can refer to the code repo of the paper to directly run the filtering with any fastText model or simply:

```python
import os
import argparse
from pathlib import Path

parser = argparse.ArgumentParser("Filter")
parser.add_argument("--input_path",type=str, help="input path name")
parser.add_argument("--output_path",type=str, help="output name")

args = parser.parse_args()
from datatrove.executor import LocalPipelineExecutor
from datatrove.pipeline.filters import FastTextClassifierFilter
from datatrove.pipeline.readers import ParquetReader,JsonlReader
from datatrove.pipeline.writers.jsonl import JsonlWriter
Path(f"{args.output_path}").mkdir(parents=True,exist_ok=True)

dist_executor = LocalPipelineExecutor(
    skip_completed=False,
    pipeline=[
        JsonlReader(f"{args.input_path}", text_key="text", default_metadata= {}),
        FastTextClassifierFilter(f"PreSelect-classifier.bin", keep_labels=[("1",0.5)]), 
        JsonlWriter(f"{args.output_path}", compression=None)
    ],
    tasks=100,
)
dist_executor.run()
```

## Training
For more training details, you can refer to the paper and the training code is available on GitHub 
[PreSelect](https://github.com/hkust-nlp/preselect).