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---
license: cc-by-sa-4.0
language:
- tig
tags:
- tigre
- language-model
- kenlm
- n-gram
---
# Tigre 3-gram Language Model (KenLM)
### Overview
This repository provides a **3-gram Language Model (LM)** for the **Tigre** language, trained using the **KenLM** toolkit. This model is a foundational resource for various downstream NLP and speech applications, including:
- Rescoring hypotheses in Automatic Speech Recognition (ASR).
- Improving text generation and fluency in Machine Translation (MT).
- Performing basic text filtering and quality control.
The model is provided in the highly optimized binary (`.arpa`) format, making it suitable for efficient use in production environments.
## Model Statistics
This language model was trained using KenLM on the **Tigre Monolingual Text Dataset (Tigre-Data 1.0)**.
| Statistic | Value |
| :----------------------------------- | :-------- |
| **Model Order** | 3-gram |
| **Vocabulary Size (Unique 1-grams)** | 316,548 |
| **Total Unique N-grams (1-to-3)** | 1,285,462 |
| **Example Perplexity** (on 'ቤት') | 147.12 |
_Note: The total raw training tokens used for this model can be found in the Tigre Monolingual Text Dataset card (approximately 14.7 million tokens)._
## Training Data Source
This model was trained exclusively on the **BeitTigreAI/tigre-data-monolingual-text** dataset.
More detailed information about the training data, including its domain, bias, preprocessing steps, and source statistics, can be found in the dataset's documentation:
[Tigre Monolingual Text Dataset README](https://huggingface.co/datasets/BeitTigreAI/tigre-data-monolingual-text/blob/main/README.md)
---
## Files and Structure
The repository contains the following files:
tigre-data-kenLM/
├── README.md
├── hf_readme.ipynb
└── tigre-data-kenLM.arpa
## How to Use the Model
You can load and query the model using the Python bindings for **KenLM** (`kenlm`).
### Installation
To use the model in Python, install the KenLM bindings:
```bash
!pip install kenlm
## Example Usage (Perplexity and Score)
The following Python code demonstrates how to load the model and query it for log probability and perplexity:
```python
import kenlm
from huggingface_hub import hf_hub_download
# 1. Download the ARPA model file from the Hugging Face Hub
arpa_path = hf_hub_download(
repo_id="BeitTigreAI/tigre-data-kenLM",
filename="tigre-data-kenLM.arpa",
repo_type="model"
)
# 2. Load the KenLM model
lm = kenlm.Model(arpa_path)
# Example single sentence to score
test_sentence = "ዕርቃን ሓይልን ንግሥን" # Or use one of the lines from your list
# A. Calculate Log10 Probability of the entire sentence
log_prob = lm.score(test_sentence)
print(f"Sentence: '{test_sentence}'")
print(f"Log10 Probability: {log_prob:.4f}")
# B. Calculate Perplexity of the entire sentence
perplexity = lm.perplexity(test_sentence)
print(f"Perplexity: {perplexity:.2f}")
```
## Licensing and Citation
The Tigre 3-gram Language Model is licensed under CC-BY-SA-4.0.
## Citation
If you use this resource in your work, please cite the repository by referencing its Hugging Face entry:
## Recommended Citation Format:
## Repository Name: Tigre 3-gram Language Model (KenLM)
## Organization: BeitTigreAI
URL: https://huggingface.co/datasets/BeitTigreAI/tigre-data-kenLM
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