Update README.md

README.md

@@ -13,6 +13,10 @@ pretty_name: French Local Administrations Directory
 size_categories:
 - 10K<n<100K
 license: etalab-2.0
+configs:
+- config_name: latest
+  data_files: "data/local-administrations-directory-latest/*.parquet"
+  default: true
 ---
 
 # 🇫🇷 French Local Administrations Directory Dataset
@@ -89,8 +93,20 @@ The resulting embedding vector is stored in the `embeddings_bge-m3` column as a
 ## 📌 Embeddings Notice
 
 ⚠️ The `embeddings_bge-m3` column is stored as a stringified list (e.g., `"[-0.03062629,-0.017049594,...]"`).  
-To use it as a vector, you need to parse it into a list of floats or NumPy array. For example:
+To use it as a vector, you need to parse it into a list of floats or NumPy array. For example, if you want to load the dataset into a dataframe by using the `datasets` library:
 
+```python
+import pandas as pd
+import json
+from datasets import load_dataset
+# The Pyarrow library must be installed in your Python environment for this example. By doing => pip install pyarrow
+
+dataset = load_dataset("AgentPublic/local-administrations-directory")
+df = pd.DataFrame(dataset['train'])
+df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
+```
+
+Otherwise, if you already downloaded all parquet files from the `data/local-administrations-directory-latest/` folder : 
 ```python
 import pandas as pd
 import json
@@ -100,6 +116,8 @@ df = pd.read_parquet(path="local-administrations-directory-latest/") # Assuming
 df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
 ```
 
+You can then use the dataframe as you wish, such as by inserting the data from the dataframe into the vector database of your choice.
+
 ## 📚 Source & License
 
 ## 🔗 Source :