---
description: "Identify document languages accurately using FastText models supporting 176 languages for multilingual text processing"
categories: ["how-to-guides"]
tags: ["language-identification", "fasttext", "multilingual", "176-languages", "detection", "classification"]
personas: ["data-scientist-focused", "mle-focused"]
difficulty: "intermediate"
content_type: "how-to"
modality: "text-only"
---

# Language Identification

Large unlabeled text corpora often contain a variety of languages. NVIDIA NeMo Curator provides tools to accurately identify the language of each document, which is essential for language-specific curation tasks and building high-quality monolingual datasets.

## How it Works

NeMo Curator's language identification system works through a three-step process:

1. **Text Preprocessing**: For FastText classification, normalize input text by stripping whitespace and converting newlines to spaces.

2. **FastText Language Detection**: The pre-trained FastText language identification model ([`lid.176.bin`](https://fasttext.cc/docs/en/language-identification.html)) analyzes the preprocessed text and returns:
   - A confidence score (0.0 to 1.0) indicating certainty of the prediction
   - A language code (for example, "EN", "ES", "FR") in FastText's two-letter uppercase format

3. **Filtering and Scoring**: The pipeline filters documents based on a configurable confidence threshold (`min_langid_score`) and stores both the confidence score and language code as metadata.

### Language Detection Process

The `FastTextLangId` filter implements this workflow by:

- Loading the FastText language identification model on worker initialization
- Processing text through `model.predict()` with `k=1` to get the top language prediction
- Extracting the language code from FastText labels (for example, `__label__en` becomes "EN")
- Comparing confidence scores against the threshold to determine document retention
- Returning results as `[confidence_score, language_code]` for downstream processing

This approach supports **176 languages** with high accuracy, making it suitable for large-scale multilingual dataset curation where language-specific processing and monolingual dataset creation are critical.

## Usage

The following example demonstrates how to create a language identification pipeline using Curator with distributed processing.

<Tabs>
<Tab title="Python">

```python
"""Language identification using Curator."""

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.filters import ScoreFilter
from nemo_curator.stages.text.filters.fasttext import FastTextLangId
from nemo_curator.stages.text.io.reader import JsonlReader

def create_language_identification_pipeline(data_dir: str) -> Pipeline:
    """Create a pipeline for language identification."""

    # Define pipeline
    pipeline = Pipeline(
        name="language_identification",
        description="Identify document languages using FastText"
    )

    # Add stages
    # 1. Reader stage - creates tasks from JSONL files
    pipeline.add_stage(
        JsonlReader(
            file_paths=data_dir,
            files_per_partition=2,  # Each task processes 2 files
        )
    )

    # 2. Language identification with filtering
    # IMPORTANT: Download lid.176.bin or lid.176.ftz from https://fasttext.cc/docs/en/language-identification.html
    fasttext_model_path = "/path/to/lid.176.bin"  # or lid.176.ftz (compressed)
    pipeline.add_stage(
        ScoreFilter(
            FastTextLangId(model_path=fasttext_model_path, min_langid_score=0.3),
            score_field="language"
        )
    )

    return pipeline

def main():
    # Create pipeline
    pipeline = create_language_identification_pipeline("./data")

    # Print pipeline description
    print(pipeline.describe())

    # Create executor and run
    results = pipeline.run()

    # Process results

    total_documents = sum(task.num_items for task in results) if results else 0
    print(f"Total documents processed: {total_documents}")

    # Access language scores
    for i, batch in enumerate(results):
        if batch.num_items &gt;0:
            df = batch.to_pandas()
            print(f"Batch {i} columns: {list(df.columns)}")
            # Language scores are now in the 'language' field

if __name__ == "__main__":
    main()
```

</Tab>
</Tabs>

## Understanding Results

The language identification process adds a score field to each document batch:

1. **`language` field**: Contains the FastText language identification results as a string representation of a list with two elements (for backend compatibility):
   - Element 0: The confidence score (between 0 and 1)
   - Element 1: The language code in FastText format (for example, "EN" for English, "ES" for Spanish)

2. **Task-based processing**: Curator processes documents in batches (tasks), and results are available through the task's Pandas DataFrame:

```python
# Access results from pipeline execution
for batch in results:
    df = batch.to_pandas()
    # Language scores are in the 'language' column
    print(df[['text', 'language']].head())
```

<Tip>
For quick exploratory inspection, converting a `DocumentBatch` to a Pandas DataFrame is fine. For performance and scalability, write transformations as `ProcessingStage`s (or with the `@processing_stage` decorator) and run them inside a `Pipeline` with an executor. Curator’s parallelism and resource scheduling apply when code runs as pipeline stages; ad‑hoc Pandas code executes on the driver and will not scale.
</Tip>