Datasets:

BeitTigreAI
/

tigre-data-parallel-multilingual

@@ -5,125 +5,19 @@ language:
 task_categories:
 - translation
 ---
-<br>
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Dataset Analysis</title>
-    <style>
-        body {
-            font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
-            background-color: #f0f2f5;
-            color: #333;
-            line-height: 1.6;
-            margin: 0;
-            padding: 20px;
-        }
-        .container {
-            max-width: 900px;
-            margin: auto;
-            background: #fff;
-            padding: 25px 30px;
-            border-radius: 12px;
-            box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
-        }
-        h1, h2, h3 {
-            color: #2c3e50;
-            border-bottom: 2px solid #e0e0e0;
-            padding-bottom: 10px;
-            margin-top: 25px;
-        }
-        pre {
-            background-color: #f4f4f9;
-            border: 1px solid #ddd;
-            border-left: 5px solid #007bff;
-            padding: 15px;
-            overflow-x: auto;
-            font-family: 'Courier New', Courier, monospace;
-            white-space: pre-wrap;
-            word-wrap: break-word;
-            border-radius: 8px;
-        }
-        table {
-            width: 100%;
-            border-collapse: collapse;
-            margin: 20px 0;
-            font-size: 1em;
-            text-align: left;
-            border-radius: 8px;
-            overflow: hidden;
-            box-shadow: 0 2px 8px rgba(0,0,0,0.1);
-        }
-        th, td {
-            padding: 12px 15px;
-            border-bottom: 1px solid #ddd;
-        }
-        thead tr {
-            background-color: #007bff;
-            color: #ffffff;
-            text-align: left;
-        }
-        tbody tr:nth-child(even) {
-            background-color: #f2f2f2;
-        }
-        tbody tr:hover {
-            background-color: #e9ecef;
-            cursor: pointer;
-        }
-        .code-block {
-            margin-bottom: 20px;
-            background: #2d2d2d;
-            color: #f8f8f2;
-            padding: 15px;
-            border-radius: 8px;
-            overflow-x: auto;
-        }
-        .code-block code {
-            font-family: 'Fira Code', 'JetBrains Mono', monospace;
-            font-size: 0.9em;
-        }
-        .section-title {
-            display: flex;
-            align-items: center;
-            gap: 10px;
-            margin-bottom: 15px;
-            font-size: 1.5em;
-            font-weight: bold;
-        }
-        .info-box {
-            background-color: #eaf6ff;
-            border-left: 5px solid #007bff;
-            padding: 15px;
-            border-radius: 8px;
-            margin-bottom: 20px;
-        }
-    </style>
-</head>
-<body>
-<div class="container">
-    <header>
-        <h1>Dataset Analysis Report</h1>
-        <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>
-    </header>
-    <main>
-        <section>
-            <h2>Python Script</h2>
-            <div class="code-block">
-                <pre><code>from datasets import load_dataset
 import pandas as pd
 # Load dataset from Hugging Face Hub
 dataset = load_dataset(
-    "BeitTigreAI/tigre-data-parallel-multilingual",
-    data_files={
-        "train": "train.parquet",
-        "validation": "validation.parquet"
-    }
 )
 # Print dataset info
@@ -134,112 +28,44 @@ print(dataset)
 print("\n🔍 Number of samples per target language:")
 for split in ["train", "validation"]:
-    print(f"\n➡️  {split.title()} Split:")
-    df = dataset[split].to_pandas()
-    # Count by tgt_lang
-    lang_counts = df["tgt_lang"].value_counts().to_frame().reset_index()
-    lang_counts.columns = ["tgt_lang", "count"]
-    lang_counts["percentage"] = (lang_counts["count"] / lang_counts["count"].sum() * 100).round(2)
-    # Print as table
-    print(lang_counts.to_string(index=False))
-</code></pre>
-            </div>
-        </section>
-        <section>
-            <div class="section-title">
-                📊 Dataset Info:
-            </div>
-            <div class="info-box">
-                <pre><code>DatasetDict({
-    train: Dataset({
-        features: ['id', 'src', 'tgt', 'src_lang', 'tgt_lang'],
-        num_rows: 283180
-    })
-    validation: Dataset({
-        features: ['id', 'src', 'tgt', 'src_lang', 'tgt_lang'],
-        num_rows: 6964
-    })
-})</code></pre>
-            </div>
-        </section>
-        <section>
-            <div class="section-title">
-                🔍 Number of samples per target language:
-            </div>
-            <h3>➡️ Train Split:</h3>
-            <table>
-                <thead>
-                    <tr>
-                        <th>tgt_lang</th>
-                        <th>count</th>
-                        <th>percentage</th>
-                    </tr>
-                </thead>
-                <tbody>
-                    <tr>
-                        <td>eng_Latn</td>
-                        <td>132323</td>
-                        <td>46.73</td>
-                    </tr>
-                    <tr>
-                        <td>deu_Latn</td>
-                        <td>67335</td>
-                        <td>23.78</td>
-                    </tr>
-                    <tr>
-                        <td>ara_Arab</td>
-                        <td>43204</td>
-                        <td>15.26</td>
-                    </tr>
-                    <tr>
-                        <td>swe_Latn</td>
-                        <td>26762</td>
-                        <td>9.45</td>
-                    </tr>
-                    <tr>
-                        <td>nob_Latn</td>
-                        <td>7548</td>
-                        <td>2.67</td>
-                    </tr>
-                    <tr>
-                        <td>nno_Latn</td>
-                        <td>5530</td>
-                        <td>1.95</td>
-                    </tr>
-                    <tr>
-                        <td>tir_Ethi</td>
-                        <td>478</td>
-                        <td>0.17</td>
-                    </tr>
-                </tbody>
-            </table>
-            <h3>➡️ Validation Split:</h3>
-            <table>
-                <thead>
-                    <tr>
-                        <th>tgt_lang</th>
-                        <th>count</th>
-                        <th>percentage</th>
-                    </tr>
-                </thead>
-                <tbody>
-                    <tr>
-                        <td>eng_Latn</td>
-                        <td>6964</td>
-                        <td>100.0</td>
-                    </tr>
-                </tbody>
-            </table>
-        </section>
-    </main>
-</div>
-</body>
-</html>

 task_categories:
 - translation
 ---
+put this in nice html format
+from datasets import load_dataset
 import pandas as pd
 # Load dataset from Hugging Face Hub
 dataset = load_dataset(
+    "BeitTigreAI/tigre-data-parallel-multilingual",
+    data_files={
+        "train": "train.parquet",
+        "validation": "validation.parquet"
+    }
 )
 # Print dataset info
 print("\n🔍 Number of samples per target language:")
 for split in ["train", "validation"]:
+    print(f"\n➡️  {split.title()} Split:")
+    df = dataset[split].to_pandas()
+    # Count by tgt_lang
+    lang_counts = df["tgt_lang"].value_counts().to_frame().reset_index()
+    lang_counts.columns = ["tgt_lang", "count"]
+    lang_counts["percentage"] = (lang_counts["count"] / lang_counts["count"].sum() * 100).round(2)
+    # Print as table
+    print(lang_counts.to_string(index=False))
+📊 Dataset Info:
+DatasetDict({
+    train: Dataset({
+        features: ['id', 'src', 'tgt', 'src_lang', 'tgt_lang'],
+        num_rows: 283180
+    })
+    validation: Dataset({
+        features: ['id', 'src', 'tgt', 'src_lang', 'tgt_lang'],
+        num_rows: 6964
+    })
+})
+🔍 Number of samples per target language:
+➡️  Train Split:
+tgt_lang  count  percentage
+eng_Latn 132323       46.73
+deu_Latn  67335       23.78
+ara_Arab  43204       15.26
+swe_Latn  26762        9.45
+nob_Latn   7548        2.67
+nno_Latn   5530        1.95
+tir_Ethi    478        0.17
+➡️  Validation Split:
+tgt_lang  count  percentage
+eng_Latn   6964       100.0