Update README.md

README.md

@@ -5,19 +5,26 @@ language:
 task_categories:
 - translation
 ---
-put this in nice html format
 
+# Dataset Analysis Report
 
+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.
+
+---
+
+### Python Script
+
+```python
 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"
-    }
+    "BeitTigreAI/tigre-data-parallel-multilingual",
+    data_files={
+        "train": "train.parquet",
+        "validation": "validation.parquet"
+    }
 )
 
 # Print dataset info
@@ -28,44 +35,13 @@ 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))
-
-
-
-
-📊 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
+    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))