Update README.md

README.md

@@ -22,35 +22,98 @@ tags:
 - transformers.js
 language:
   - multilingual
+  - af
+  - am
   - ar
+  - as
+  - az
+  - be
+  - bg
   - bn
+  - br
+  - bs
+  - ca
+  - cs
+  - cy
   - da
   - de
   - el
   - en
+  - eo
   - es
+  - et
+  - eu
+  - fa
   - fi
   - fr
+  - fy
+  - ga
+  - gd
+  - gl
+  - gu
+  - ha
+  - he
   - hi
+  - hr
+  - hu
+  - hy
   - id
+  - is
   - it
   - ja
+  - jv
   - ka
+  - kk
+  - km
+  - kn
   - ko
+  - ku
+  - ky
+  - la
+  - lo
+  - lt
   - lv
+  - mg
+  - mk
+  - ml
+  - mn
+  - mr
+  - ms
+  - my
+  - ne
   - nl
   - no
+  - om
+  - or
+  - pa
   - pl
+  - ps
   - pt
   - ro
   - ru
+  - sa
+  - sd
+  - si
   - sk
+  - sl
+  - so
+  - sq
+  - sr
+  - su
   - sv
+  - sw
+  - ta
+  - te
   - th
+  - tl
   - tr
+  - ug
   - uk
   - ur
+  - uz
   - vi
+  - xh
+  - yi
   - zh
 inference: false
 ---
@@ -70,15 +133,19 @@ inference: false
 <b>Jina CLIP: your CLIP model is also your text retriever!</b>
 </p>
 
+## Quick Start
+
+[Blog](https://jina.ai/news/jina-embeddings-v3-a-frontier-multilingual-embedding-model/#parameter-dimensions) | [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.jina-clip-v2) | [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-kdi3xkt62lo32) | [API](https://jina.ai/embeddings)
+
 
 ## Intended Usage & Model Info
 
 `jina-clip-v2` is a state-of-the-art **multilingual and multimodal (text-image) embedding model**.
 
 `jina-clip-v2` is a successor to the [`jina-clip-v1`](https://huggingface.co/jinaai/jina-clip-v1) model and brings new features and capabilities, such as:
-* *support for multiple languages* - the text tower now supports 30 languages, including `en`, `zh`, `de`, `ar`, `hi`, `es`
-* *embedding truncation on both image and text vectors* - both towers are trained using [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) which enables slicing the output vectors and in as a result computation and storage costs as well
-* *visual document retrieval performance boost* - with an image resolution of 384 (compared to 224 on `jina-clip-v1`) the image tower can now capture finer visual details. This feature along with a more diverse training set enable the model to perform much better on visual document retrieval tasks, as is evident by the performance gains on the [ViDoRe Benchmark](https://huggingface.co/spaces/vidore/vidore-leaderboard), compared to `jina-clip-v1` 
+* *support for multiple languages* - the text tower now supports 100 languages with tuning focus on **Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.**
+* *embedding truncation on both image and text vectors* - both towers are trained using [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) which enables slicing the output vectors and in as a result computation and storage costs as well.
+* *visual document retrieval performance boost* - with an image resolution of 512 (compared to 224 on `jina-clip-v1`) the image tower can now capture finer visual details. This feature along with a more diverse training set enable the model to perform much better on visual document retrieval tasks. This enable `jina-clip-v2` as a strong encoder for future vLLM based retriever.
 
 Similar to our predecessor model, `jina-clip-v2` bridges the gap between text-to-text and cross-modal retrieval. Via a single vector space, `jina-clip-v2` offers state-of-the-art performance on both tasks.
 This dual capability makes it an excellent tool for multimodal retrieval-augmented generation (MuRAG) applications, enabling seamless text-to-text and text-to-image searches within a single model.
@@ -210,38 +277,4 @@ If you find `jina-clip-v2` useful in your research, please cite the following pa
     Year = {2024},
     Eprint = {arXiv:2405.20204},
 }
-```
-
-## FAQ
-
-### I encounter this problem, what should I do?
-
-```
-ValueError: The model class you are passing has a `config_class` attribute that is not consistent with the config class you passed (model has <class 'transformers_modules.jinaai.jina-clip-implementation.7f069e2d54d609ef1ad2eb578c7bf07b5a51de41.configuration_clip.JinaCLIPConfig'> and you passed <class 'transformers_modules.jinaai.jina-clip-implementation.7f069e2d54d609ef1ad2eb578c7bf07b5a51de41.configuration_cli.JinaCLIPConfig'>. Fix one of those so they match!
-```
-
-There was a bug in Transformers library between 4.40.x to 4.41.1. You can update transformers to >4.41.2 or <=4.40.0
-
-### Given one query, how can I merge its text-text and text-image cosine similarity?
-
-Our emperical study shows that text-text cosine similarity is normally larger than text-image cosine similarity!
-If you want to merge two scores, we recommended 2 ways:
-
-1. weighted average of text-text sim and text-image sim:
-
-```python
-combined_scores = sim(text, text) + lambda * sim(text, image)  # optimal lambda depends on your dataset, but in general lambda=2 can be a good choice.
-```
-
-2. apply z-score normalization before merging scores:
-
-```python
-# pseudo code
-query_document_mean = np.mean(cos_sim_text_texts)
-query_document_std = np.std(cos_sim_text_texts)
-text_image_mean = np.mean(cos_sim_text_images)
-text_image_std = np.std(cos_sim_text_images)
-
-query_document_sim_normalized = (cos_sim_query_documents - query_document_mean) / query_document_std
-text_image_sim_normalized = (cos_sim_text_images - text_image_mean) / text_image_std
-```
+```