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Datasets:
BeitTigreAI
/
tigre-data-parallel-multilingual

Tasks:
Translation
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parquet
Languages:
Tigre
Size:
100K - 1M
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@@ -5,8 +5,116 @@ language:
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  task_categories:
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  - translation
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  ---
 
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- from datasets import load_dataset
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  import pandas as pd
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  # Load dataset from Hugging Face Hub
@@ -36,12 +144,16 @@ for split in ["train", "validation"]:
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  # Print as table
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  print(lang_counts.to_string(index=False))
 
 
 
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-
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-
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-
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- 📊 Dataset Info:
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- DatasetDict({
 
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  train: Dataset({
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  features: ['id', 'src', 'tgt', 'src_lang', 'tgt_lang'],
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  num_rows: 283180
@@ -50,20 +162,84 @@ DatasetDict({
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  features: ['id', 'src', 'tgt', 'src_lang', 'tgt_lang'],
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  num_rows: 6964
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  })
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- })
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- 🔍 Number of samples per target language:
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ➡️ Train Split:
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- tgt_lang count percentage
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- eng_Latn 132323 46.73
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- deu_Latn 67335 23.78
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- ara_Arab 43204 15.26
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- swe_Latn 26762 9.45
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- nob_Latn 7548 2.67
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- nno_Latn 5530 1.95
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- tir_Ethi 478 0.17
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- ➡️ Validation Split:
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- tgt_lang count percentage
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- eng_Latn 6964 100.0
 
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  task_categories:
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  - translation
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  ---
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+ <br>
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+ <!DOCTYPE html>
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+ <html lang="en">
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+ <head>
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+ <meta charset="UTF-8">
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+ <meta name="viewport" content="width=device-width, initial-scale=1.0">
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+ <title>Dataset Analysis</title>
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+ <style>
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+ body {
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+ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
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+ background-color: #f0f2f5;
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+ color: #333;
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+ line-height: 1.6;
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+ margin: 0;
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+ padding: 20px;
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+ }
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+ .container {
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+ max-width: 900px;
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+ margin: auto;
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+ background: #fff;
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+ padding: 25px 30px;
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+ border-radius: 12px;
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+ box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
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+ }
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+ h1, h2, h3 {
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+ color: #2c3e50;
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+ border-bottom: 2px solid #e0e0e0;
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+ padding-bottom: 10px;
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+ margin-top: 25px;
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+ }
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+ pre {
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+ background-color: #f4f4f9;
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+ border: 1px solid #ddd;
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+ border-left: 5px solid #007bff;
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+ padding: 15px;
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+ white-space: pre-wrap;
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+ word-wrap: break-word;
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+ border-radius: 8px;
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+ table {
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+ width: 100%;
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+ border-collapse: collapse;
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+ margin: 20px 0;
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+ font-size: 1em;
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+ text-align: left;
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+ border-radius: 8px;
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+ overflow: hidden;
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+ box-shadow: 0 2px 8px rgba(0,0,0,0.1);
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+ }
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+ th, td {
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+ padding: 12px 15px;
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+ border-bottom: 1px solid #ddd;
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+ }
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+ thead tr {
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+ background-color: #007bff;
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+ color: #ffffff;
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+ text-align: left;
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+ }
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+ tbody tr:nth-child(even) {
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+ background-color: #f2f2f2;
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+ }
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+ tbody tr:hover {
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+ }
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+ .code-block {
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+ margin-bottom: 20px;
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+ background: #2d2d2d;
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+ color: #f8f8f2;
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+ padding: 15px;
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+ border-radius: 8px;
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+ overflow-x: auto;
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+ }
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+ .code-block code {
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+ font-family: 'Fira Code', 'JetBrains Mono', monospace;
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+ font-size: 0.9em;
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+ }
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+ .section-title {
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+ display: flex;
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+ align-items: center;
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+ gap: 10px;
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+ margin-bottom: 15px;
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+ font-size: 1.5em;
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+ padding: 15px;
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+ border-radius: 8px;
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+ margin-bottom: 20px;
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+ }
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+ </style>
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+ </head>
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+ <body>
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+
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+ <div class="container">
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+ <header>
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+ <h1>Dataset Analysis Report</h1>
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+ <p>This report presents an analysis of the 'tigre-data-parallel-multilingual' dataset from the Hugging Face Hub, including dataset information and sample distribution by target language.</p>
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+ </header>
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+
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+ <main>
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+ <section>
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+ <h2>Python Script</h2>
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+ <div class="code-block">
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+ <pre><code>from datasets import load_dataset
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  import pandas as pd
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  # Load dataset from Hugging Face Hub
 
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  # Print as table
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  print(lang_counts.to_string(index=False))
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+ </code></pre>
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+ </div>
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+ </section>
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+ <section>
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+ <div class="section-title">
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+ 📊 Dataset Info:
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+ </div>
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+ <div class="info-box">
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+ <pre><code>DatasetDict({
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  train: Dataset({
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  features: ['id', 'src', 'tgt', 'src_lang', 'tgt_lang'],
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  num_rows: 283180
 
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  features: ['id', 'src', 'tgt', 'src_lang', 'tgt_lang'],
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  num_rows: 6964
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  })
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+ })</code></pre>
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+ </div>
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+ </section>
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+
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+ <section>
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+ <div class="section-title">
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+ 🔍 Number of samples per target language:
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+ </div>
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+
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+ <h3>➡️ Train Split:</h3>
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+ <table>
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+ <thead>
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+ <tr>
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+ <th>tgt_lang</th>
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+ <th>count</th>
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+ <th>percentage</th>
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+ </tr>
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+ </thead>
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+ <tbody>
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+ <tr>
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+ <td>eng_Latn</td>
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+ <td>132323</td>
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+ <td>46.73</td>
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+ </tr>
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+ <tr>
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+ <td>deu_Latn</td>
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+ <td>67335</td>
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+ <td>23.78</td>
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+ </tr>
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+ <tr>
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+ <td>ara_Arab</td>
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+ <td>43204</td>
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+ <td>15.26</td>
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+ </tr>
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+ <tr>
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+ <td>swe_Latn</td>
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+ <td>26762</td>
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+ <td>9.45</td>
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+ </tr>
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+ <tr>
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+ <td>nob_Latn</td>
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+ <td>7548</td>
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+ <td>2.67</td>
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+ </tr>
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+ <tr>
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+ <td>nno_Latn</td>
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+ <td>5530</td>
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+ <td>1.95</td>
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+ </tr>
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+ <tr>
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+ <td>tir_Ethi</td>
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+ <td>478</td>
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+ <td>0.17</td>
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+ </tr>
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+ </tbody>
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+ </table>
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+ <h3>➡️ Validation Split:</h3>
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+ <table>
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+ <thead>
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+ <tr>
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+ <th>tgt_lang</th>
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+ <th>count</th>
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+ <th>percentage</th>
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+ </tr>
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+ </thead>
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+ <tbody>
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+ <tr>
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+ <td>eng_Latn</td>
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+ <td>6964</td>
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+ <td>100.0</td>
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+ </tr>
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+ </tbody>
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+ </table>
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+ </section>
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+ </main>
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+ </div>
 
 
 
 
 
 
 
 
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+ </body>
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+ </html>