Update README.md

README.md

@@ -60,20 +60,26 @@ This dataset is a derivative work created by fusing and enriching the following
 * [The Movies Dataset](https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset) by Rounak Banik.
 * [IMDB Multimodal Vision & NLP Genre Classification](https://www.kaggle.com/datasets/zulkarnainsaurav/imdb-multimodal-vision-and-nlp-genre-classification) by Zulkarnain Saurav.
 
-### Annotations
+### Embedding Generation Process
 
-The dataset was annotated automatically. The primary annotation is the `content_embedding` column, generated as described above. Due to the nature of the source data, some fields (like `poster_byte`) may be sparse.
+The primary `content_embedding` column was generated through a multi-step process:
+
+1.  **Initial Feature Extraction:** For each movie, initial embeddings were generated from different modalities using powerful pre-trained models.
+    * **Text Embeddings:** Plot descriptions, taglines, and cast/crew information were passed through a `sentence-transformers/all-MiniLM-L6-v2` model.
+    * **Image Embeddings:** Movie posters were passed through the image encoder of the `openai/clip-vit-base-patch32` model. For movies without a poster, a zero vector was used as a neutral placeholder.
+2.  **Fusion Model Training:** These separate, high-dimensional vectors were concatenated and fed into a custom fusion model (a Multi-Layer Perceptron). This fusion model was then trained using a multi-task triplet loss objective based on genre, director, and actor similarity.
+3.  **Final Embedding Generation:** The `content_embedding` in this dataset is the final 512-dimensional output of this trained fusion model, representing a rich, learned combination of all input modalities.
 
 ## Citation Information
 
 If you use this dataset in your research, please cite it as follows:
 
 ```bibtex
-@misc{[your_username_2025_enriched_movie_embeddings],
-  author = {[Your Name Here]},
-  title = {Enriched Movie Dataset with Multimodal Embeddings},
-  year = {2025},
-  publisher = {Hugging Face},
-  journal = {Hugging Face repository},
-  howpublished = {\url{[https://huggingface.co/datasets/](https://huggingface.co/datasets/ujwal-jibhkate/enriched-movie-dataset-with-multimodal-embeddings/)}},
+@misc{jibhkate2025enriched,
+  author       = {Ujwal Jibhkate},
+  title        = {Enriched Movie Dataset with Multimodal Embeddings},
+  year         = {2025},
+  publisher    = {Hugging Face},
+  journal      = {Hugging Face repository},
+  howpublished = {\url{[https://huggingface.co/datasets/ujwal-jibhkate/enriched-movie-dataset-with-multimodal-embeddings](https://huggingface.co/datasets/ujwal-jibhkate/enriched-movie-dataset-with-multimodal-embeddings)}},
 }