Update README.md

README.md

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----
-license: apache-2.0
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+---
+tags:
+  - feature-extraction
+  - sentence-similarity
+  - mteb
+language: en
+inference: false
+license: apache-2.0
+---
+<!-- TODO: add evaluation results here -->
+<br><br>
+
+<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>
+
+
+<p align="center">
+<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
+</p>
+
+## Quick Start
+
+The easiest way to starting using `jina-clip-v1` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
+
+## Intended Usage & Model Info
+
+### `jina-clip-v1` Overview
+
+### `jina-clip-v1` Overview
+
+`jina-clip-v1` is an English, monolingual **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.
+
+`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.
+
+
+## Data & Parameters
+
+Jina CLIP V1 [technical report]() coming soon.
+
+## Usage
+
+You can use Jina CLIP directly from transformers package.
+
+```python
+!pip install transformers
+from transformers import AutoModel
+from numpy.linalg import norm
+
+cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
+model = AutoModel.from_pretrained('jinaai/jina-clip-v1')
+text_embeddings = model.encode_text(['How is the weather today?', 'What is the current weather like today?'])
+image_embeddings = model.encode_image(['raindrop.png'])
+print(cos_sim(text_embeddings[0], text_embeddings[1])) # text embedding similarity
+print(cos_sim(text_embeddings[0], image_embeddings[0])) # text-image cross-modal similarity
+```
+
+
+## Contact
+
+Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
+
+## Citation
+
+If you find Jina CLIP useful in your research, please cite the following paper:
+
+```console
+TBD
+```
+