Update README.md

README.md

@@ -4,7 +4,115 @@ language:
 - tig
 tags:
 - tigre
-- africa
-- low-resource
+- 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 <-- This file
+├── hf_readme.ipynb <-- Jupyter notebook used for statistics extraction
+└── tigre-data-kenLM.arpa <-- The primary language model file (ARPA format)
+
+## 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