Update README.md

README.md

@@ -32,8 +32,8 @@ from transformers import CLIPTokenizer, CLIPTextModelWithProjection
 
 search_sentence = "a basketball player performing a slam dunk"
 
-model = CLIPTextModelWithProjection.from_pretrained("Diangle/clip4clip-webvid")
-tokenizer = CLIPTokenizer.from_pretrained("Diangle/clip4clip-webvid")
+model = CLIPTextModelWithProjection.from_pretrained("Searchium-ai/clip4clip-webvid150k")
+tokenizer = CLIPTokenizer.from_pretrained("Searchium-ai/clip4clip-webvid150k")
 
 inputs = tokenizer(text=search_sentence , return_tensors="pt")
 outputs = model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])
@@ -46,14 +46,14 @@ print("final output: ", final_output)
 
 ### Extracting Video Embeddings:
 
-An additional notebook ["GSI_VideoRetrieval_VideoEmbedding.ipynb"](https://huggingface.co/Diangle/clip4clip-webvid/blob/main/Notebooks/GSI_VideoRetrieval_VideoEmbedding.ipynb), provides instructions for extracting video embeddings and includes the necessary tools for preprocessing videos.
+An additional notebook ["GSI_VideoRetrieval_VideoEmbedding.ipynb"](https://huggingface.co/Searchium-ai/clip4clip-webvid150k/blob/main/Notebooks/GSI_VideoRetrieval_VideoEmbedding.ipynb), provides instructions for extracting video embeddings and includes the necessary tools for preprocessing videos.
 
 
 ## Model Intended Use
 
 This model is intended for use in large scale video-text retrieval applications. 
 
-To illustrate its functionality, refer to the accompanying [**Video Search Space**](https://huggingface.co/spaces/Diangle/Clip4Clip-webvid) which provides a search demonstration on a vast collection of approximately 1.5 million videos. 
+To illustrate its functionality, refer to the accompanying [**Video Search Space**](https://huggingface.co/spaces/Searchium-ai/Video-Search) which provides a search demonstration on a vast collection of approximately 1.5 million videos. 
 This interactive demo showcases the model's capability to effectively retrieve videos based on text queries, highlighting its potential for handling substantial video datasets.
 
 ## Motivation
@@ -82,7 +82,7 @@ We evaluate R1, R5, R10, MedianR, and MeanR on:
 | Binarized CLIP4Clip trained on 150k Webvid with rerank100 | 50.56 | 76.39 | 83.51 | 1.0 | 43.2964
 
 For an elaborate description of the evaluation refer to the notebook 
-[GSI_VideoRetrieval-Evaluation](https://huggingface.co/Diangle/clip4clip-webvid/blob/main/Notebooks/GSI_VideoRetrieval-Evaluation.ipynb).
+[GSI_VideoRetrieval-Evaluation](https://huggingface.co/Searchium-ai/clip4clip-webvid150k/blob/main/Notebooks/GSI_VideoRetrieval-Evaluation.ipynb).
 
 <div id="footnote1">
   
@@ -93,7 +93,7 @@ For an elaborate description of the evaluation refer to the notebook
 
 
 ## Acknowledgements
-Acknowledging Diana Mazenko of [Searchium](https://www.searchium.ai) for adapting and loading the model to Hugging Face, and for creating a Hugging Face [**SPACE**](https://huggingface.co/spaces/Diangle/Clip4Clip-webvid) for a large-scale video-search demo.
+Acknowledging Diana Mazenko of [Searchium](https://www.searchium.ai) for adapting and loading the model to Hugging Face, and for creating a Hugging Face [**SPACE**](https://huggingface.co/spaces/Searchium-ai/Video-Search) for a large-scale video-search demo.
 
 Acknowledgments also to Lou et el for their comprehensive work on CLIP4Clip and openly available code.