update readme

README.md

@@ -3,7 +3,6 @@ tags:
   - sentence-transformers
   - feature-extraction
   - sentence-similarity
-  - mteb
 language:
   - de
   - en
@@ -3109,7 +3108,7 @@ model-index:
 <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">
+<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Jina AI logo: Jina AI is your Portal to Multimodal AI" width="150px">
 </p>
 
 
@@ -3117,6 +3116,9 @@ model-index:
 <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-embeddings-v2-base-de` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
 
 ## Intended Usage & Model Info
 
@@ -3135,13 +3137,17 @@ Des Weiteren stellen wir folgende Embedding-Modelle bereit:
 
 - [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
 - [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
-- [`jina-embeddings-v2-base-zh`](): Chinese-English Bilingual embeddings (soon).
-- [`jina-embeddings-v2-base-de`](): German-English Bilingual embeddings (soon) **(you are here)**.
-- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon).
+- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings.
+- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings **(you are here)**.
+- _[`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon)._
+- _Bilingual embedding models in other world languages (soon)._
+- _Multimodal-input embedding model (soon)._
+- _High-performing reranking model (soon)._
 
 ## Data & Parameters
 
-Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923)
+We will publish a report with technical details about the training of the bilingual models soon.
+The training of the English model is described in this [technical report](https://arxiv.org/abs/2310.19923).
 
 ## Usage
 
@@ -3204,9 +3210,29 @@ embeddings = model.encode(
 )
 ```
 
-## Fully-managed Embeddings Service
+If you want to use the model together with the [sentence-transformers package](https://github.com/UKPLab/sentence-transformers/), make sure that you have installed the latest release and set `trust_remote_code=True` as well:
+
+```
+!pip install -U sentence-transformers
+from sentence_transformers import SentenceTransformer
+from numpy.linalg import norm
+
+cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
+model = SentenceTransformer('jinaai/jina-embeddings-v2-base-de', trust_remote_code=True)
+embeddings = model.encode(['How is the weather today?', 'Wie ist das Wetter heute?'])
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+## Alternatives to Using Transformers Package
+
+1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/). 
+2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).
+
+## Benchmark Results
 
-Alternatively, you can use Jina AI's [Embedding platform](https://jina.ai/embeddings/) for fully-managed access to Jina Embeddings models.
+We evaluated our Bilingual model on all German and English evaluation tasks availble on the [MTEB benchmark](https://huggingface.co/blog/mteb). In addition, we evaluated the models agains a couple of other German, English, and multilingual models on additional German evaluation tasks:
+
+<img src="de_evaluation_results.png" width="780px">
 
 ## Use Jina Embeddings for RAG
 
@@ -3216,13 +3242,6 @@ According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/b
 
 <img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">
 
-
-## Plans
-
-1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese.
-2. Multimodal embedding models enable Multimodal RAG applications.
-3. High-performt rerankers.
-
 ## Contact
 
 Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.