Update README.md

README.md

@@ -50,7 +50,7 @@ The dataset is provided in **Parquet format** and contains the following columns
 | `hierarchy`            | `list[dict]`              | Links to parent or child entities.                                            |
 | `directory_url`        | `str`                       | Source URL from the official state directory website.                       |
 | `chunk_text`           | `str`                       | Textual content of the administrative chunk.                                |
-| `embeddings`           | `str` (stringified list)    | Embeddings of `chunk_text` using `BAAI/bge-m3`. Stored as a JSON array string. |
+| `embeddings_bge-m3`           | `str` (stringified list)    | Embeddings of `chunk_text` using `BAAI/bge-m3`. Stored as a JSON array string. |
 
 ---
 
@@ -81,12 +81,12 @@ A synthetic text field called `chunk_text` was created to summarize key aspects
 
 There was no need here to split characters here.
 
-### 🧠 3. Embedding Generation
+### 🧠 3. Embeddings Generation
 
-Each `chunk_text` was embedded using the [**`BAAI/bge-m3`**](https://huggingface.co/BAAI/bge-m3) model. The resulting embedding vector is stored in the `embeddings` column as a **string**, but can easily be parsed back into a `list[float]` or NumPy array.
+Each `chunk_text` was embedded using the [**`BAAI/bge-m3`**](https://huggingface.co/BAAI/bge-m3) model. The resulting embedding vector is stored in the `embeddings_bge-m3` column as a **string**, but can easily be parsed back into a `list[float]` or NumPy array.
 
 ## 📌 Embeddings Notice
-⚠️ The `embeddings` 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.
+⚠️ 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, if you want to load the dataset into a dataframe :
 
 ```python
@@ -94,7 +94,7 @@ import pandas as pd
 import json
 
 df = pd.read_parquet("state_administrations_directory.parquet")
-df["embeddings"] = df["embeddings"].apply(json.loads)
+df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
 ```
 
 ## 📚 Source & License