Update README.md

README.md

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 license: apache-2.0
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 <!-- TODO: add evaluation results here -->
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 <p align="center">
 <img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
 </p>
 
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-<p align="center">
-<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
-</p>
+# jina-clip-v1
+Jina CLIP: your CLIP model is also your text retriever!
 
 ## Quick Start
 
-The easiest way to starting using `jina-clip-v1` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
+The easiest way to starting using `jina-clip-v1` is to use Jina AI [Embedding API](https://jina.ai/embeddings/).
 
 ## Intended Usage & Model Info
 
-`jina-clip-v1` is an English, monolingual **multimodal (text-image) embedding model**.
+`jina-clip-v1` is a state-of-the-art English **multimodal (text-image) embedding model**.
 
-Traditional text embedding models, such as [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en),
-excel in text-to-text retrieval but lack cross-modal retrieval capabilities.
-Conversely, CLIP-like models, such as [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32),
-align image embeddings with text embeddings but underperform in text-to-text retrieval due to their training methodology and context length limitations.
+Traditional text embedding models, such as [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en), excel in text-to-text retrieval but fall short in cross-modal tasks. In contrast, models like [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) effectively align image and text embeddings but are not optimized for text-to-text retrieval due to their training methodologies and context limitations.
 
-`jina-clip-v1` is an innovative **multimodal embedding model**.
-Its text component achieves comparable performance to `jina-embeddings-v2-base-en` in text-to-text retrieval,
-while the overall model delivers state-of-the-art performance in cross-modal retrieval tasks.
-This makes it an ideal choice for multimodal retrieval-augmented generation (M-RAG) applications,
-allowing for both text-to-text and text-to-image searches with a single model.
+`jina-clip-v1` bridges this gap by offering robust performance in both domains. Its text component matches the retrieval efficiency of `jina-embeddings-v2-base-en`, while its overall architecture sets a new benchmark for cross-modal retrieval. This dual capability makes it an excellent tool for multimodal retrieval-augmented generation (M-RAG) applications, enabling seamless text-to-text and text-to-image searches within a single model.
 
 
 ## Data & Parameters