Update README.md

README.md

@@ -86,8 +86,6 @@ By providing a standardized dataset of global legal texts, we aim to accelerate
 
 ## Dataset Structure
 
-# Dataset Structure
-
 The dataset contains the following columns:
 
 1. **jurisdiction**: Capitalized ISO 3166-1 alpha-2 code representing the country or jurisdiction. This column is useful when stacking data from different jurisdictions.
@@ -107,7 +105,9 @@ The dataset contains the following columns:
 15. **formatted_date**: The publication date formatted as 'YYYY-MM-DD HH:MM:SS', derived from the 'date_publication' column.
 
 This structure ensures comprehensive metadata for each legal document, facilitating easier data management, cross-referencing, and analysis across different jurisdictions and languages.
-Easy-to-use script for hashing the `document`:
+Easy-to-use script for hashing the `text`:
+
+Datasets version:
 
 ```python
 import hashlib
@@ -137,9 +137,86 @@ def hash(
 dataset = dataset.map(lambda x: {"hash": hash(x["document"])})
 ```
 
-## Country-based Splits
+Polars version
+
+```python
+import polars as pl
+import hashlib
+
+def add_text_hash_column(
+    df: pl.DataFrame,
+    text_column: str = "text",
+    hash_column: str = "text_hash"
+) -> pl.DataFrame:
+    """
+    Add a column with SHA-256 hash values of a specified text column to a Polars DataFrame.
+
+    This function computes the SHA-256 hash of the values in the specified text column
+    and adds it as a new column to the DataFrame.
 
-The dataset uses country-based splits to organize legal documents from different jurisdictions. Each split is identified by the ISO 3166-1 alpha-2 code of the corresponding country.
+    Parameters
+    ----------
+    df : pl.DataFrame
+        The input Polars DataFrame.
+    text_column : str, optional
+        The name of the column containing the text to be hashed (default is "text").
+    hash_column : str, optional
+        The name of the new column to be added with hash values (default is "text_hash").
+
+    Returns
+    -------
+    pl.DataFrame
+        A new DataFrame with the hash column added.
+
+    Examples
+    --------
+    >>> import polars as pl
+    >>> df = pl.DataFrame({"text": ["Hello", "World", "OpenAI"]})
+    >>> df_with_hash = add_text_hash_column(df)
+    >>> print(df_with_hash)
+    shape: (3, 2)
+    ┌────────┬──────────────────────────────────────────────────────────────────┐
+    │ text   ┆ text_hash                                                        │
+    │ ---    ┆ ---                                                              │
+    │ str    ┆ str                                                              │
+    ╞════════╪══════════════════════════════════════════════════════════════════╡
+    │ Hello  ┆ 185f8db32271fe25f561a6fc938b2e264306ec304eda518007d1764826381969 │
+    │ World  ┆ 78ae647dc5544d227130a0682a51e30bc7777fbb6d8a8f17007463a3ecd1d524 │
+    │ OpenAI ┆ 0c3f4a61f7e5d29abc29d63f1a0cad36c49ffd5b5c0b5b38ce0c7aa0bdc94696 │
+    └────────┴──────────────────────────────────────────────────────────────────┘
+
+    Raises
+    ------
+    ValueError
+        If the specified text_column is not found in the DataFrame.
+    """
+    if text_column not in df.columns:
+        raise ValueError(f"Column '{text_column}' not found in the DataFrame.")
+
+    return df.with_columns(
+        pl.col(text_column)
+        .map_elements(lambda x: hashlib.sha256(str(x).encode()).hexdigest())
+        .alias(hash_column)
+    )
+
+dataframe = add_text_hash_column(dataframe)
+```
+
+## Upload to the Hub
+
+Here is a code snippet to push dedicated config (in this example for France) to the HF hub:
+
+```python
+hf_dataset.push_to_hub(
+    repo_id="HFforLegal/laws-new",
+    config_name="fr",
+    split="train",
+)
+```
+
+## Country-based Configs
+
+The dataset uses country-based configs to organize legal documents from different jurisdictions. Each config is identified by the ISO 3166-1 alpha-2 code of the corresponding country.
 
 ### ISO 3166-1 alpha-2 Codes
 
@@ -156,20 +233,6 @@ Some examples of ISO 3166-1 alpha-2 codes:
 
 Before submitting a new split, please make sure the proposed split fits within the ISO code for the related country. 
 
-### Accessing Country-specific Data
-
-To access legal documents for a specific country, you can use the country's ISO 3166-1 alpha-2 code as the split name when loading the dataset. Here's an example:
-
-```python
-from datasets import load_dataset
-
-# Load the entire dataset
-dataset = load_dataset("HFforLegal/laws")
-
-# Access the French legal documents
-fr_dataset = dataset['fr']
-```
-
 ## Ethical Considerations
 
 While this dataset provides a valuable resource for legal AI development, users should be aware of the following ethical considerations: