Update README.md

README.md

@@ -18,13 +18,9 @@ This repository provides a **3-gram Language Model (LM)** for the **Tigre** lang
 - Rescoring hypotheses in Automatic Speech Recognition (ASR).
 - Improving text generation and fluency in Machine Translation (MT).
 - Performing basic text filtering and quality control.
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 The model is provided in the highly optimized binary (`.arpa`) format, making it suitable for efficient use in production environments.
 
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 ## Model Statistics
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 This language model was trained using KenLM on the **Tigre Monolingual Text Dataset (Tigre-Data 1.0)**.
 
 | Statistic                            | Value     |
@@ -36,12 +32,8 @@ This language model was trained using KenLM on the **Tigre Monolingual Text Data
 
 _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)._
 
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 ## Training Data Source
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 This model was trained exclusively on the **BeitTigreAI/tigre-data-monolingual-text** dataset.
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 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)
 
@@ -50,20 +42,16 @@ More detailed information about the training data, including its domain, bias, p
 ## 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
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 You can load and query the model using the Python bindings for **KenLM** (`kenlm`).
 
 ### Installation
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 To use the model in Python, install the KenLM bindings:
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 ```bash
 !pip install kenlm
 
@@ -71,28 +59,22 @@ To use the model in Python, install the KenLM bindings:
 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
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 # 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}")
@@ -100,11 +82,9 @@ print(f"Perplexity: {perplexity:.2f}")
 ```
 
 ## Licensing and Citation
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 The Tigre 3-gram Language Model is licensed under CC-BY-SA-4.0.
 
 ## Citation
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 If you use this resource in your work, please cite the repository by referencing its Hugging Face entry:
 
 ## Recommended Citation Format:
@@ -112,5 +92,4 @@ If you use this resource in your work, please cite the repository by referencing
 ## Repository Name: Tigre 3-gram Language Model (KenLM)
 
 ## Organization: BeitTigreAI
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 URL: https://huggingface.co/datasets/BeitTigreAI/tigre-data-kenLM