BeitTigreAI/tigre-data-kenLM
Tigre 5-gram Language Model (KenLM) Overview This repository provides a 5-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… See the full description on the dataset page: https://huggingface.co/datasets/BeitTigreAI/tigre-data-kenLM.
Tigre 5-gram Language Model (KenLM)
Overview
This repository provides a 5-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 standard ARPA format, and can be converted to a compact binary format for efficient use in production environments.
Model Statistics
Training Data Source
This model was trained on a cleaned Tigre monolingual text dataset, combining Tigre-language news text with additional narrative text, to give the model broader coverage than news alone.
More detailed information about the training data can be found in the dataset's documentation: Tigre Monolingual Text Dataset README
Files and Structure
tigre-data-kenLM/ ├── README.md └── tigre-data-kenLM.arpa
How to Use the Model
You can load and query the model using the Python bindings for KenLM (kenlm).
Installation
pip install kenlm huggingface_hubExample Usage (Perplexity and Score)
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 sentence to score
test_sentence = "ህተ ትብል ሑዬቱ ወአነ እብል ሕቼተ"
# A. 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. Perplexity of the entire sentence
perplexity = lm.perplexity(test_sentence)
print(f"Perplexity: {perplexity:.2f}")Licensing and Citation
The Tigre 5-gram Language Model is licensed under CC-BY-SA-4.0.
Recommended Citation Format
Repository Name: Tigre 5-gram Language Model (KenLM) Organization: BeitTigreAI URL: https://huggingface.co/BeitTigreAI/tigre-data-kenLM
