Update README.md

README.md

@@ -8,7 +8,7 @@ tags:
 - embeddings
 - open-data
 - government
-pretty_name: Travail Emploi website Dataset (French Minister of Labor and Employment)
+pretty_name: French Minister of Labor and Employment's website Dataset (Travail Emploi)
 size_categories:
 - 1K<n<10K
 license: etalab-2.0
@@ -42,7 +42,7 @@ The dataset is provided in **Parquet format** and includes the following columns
 | `context`      | `list[str]`      | Section names related to the chunk.              |
 | `text`         | `str`            | Textual content extracted and chunked from a section of the article.    |
 | `chunk_text`   | `str`            | Formated text including `title`, `context`, `introduction` and `text` values for embedding   |
-| `embeddings`   | `str`            | Embedding vector of `chunk_text` using `BAAI/bge-m3`, stored as JSON array string.  |
+| `embeddings_bge-m3`   | `str`            | Embedding vector of `chunk_text` using `BAAI/bge-m3`, stored as JSON array string.  |
 
 ---
 ## 🛠️ Data Processing Methodology
@@ -75,13 +75,13 @@ The Langchain's `RecursiveCharacterTextSplitter` function was used to make these
 - `chunk_overlap` = 20
 - `length_function` = len
 
-### 🧠 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.
 
 ## 📌 Embedding Use Notice
 
-⚠️ The `embeddings` column is stored as a **stringified list** of floats (e.g., `"[-0.03062629,-0.017049594,...]"`). 
+⚠️ The `embeddings_bge-m3` column is stored as a **stringified list** of floats (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
@@ -89,7 +89,7 @@ import pandas as pd
 import json
 
 df = pd.read_parquet("travail_emploi.parquet")
-df["embeddings"] = df["embeddings"].apply(json.loads)
+df["embeddings_bge-m3"] = df["embeddings_bge-m3"].apply(json.loads)
 ```
 
 ## 📚 Source & License