Update README.md

README.md

@@ -2611,19 +2611,19 @@ model-index:
 <br><br>
 
 <p align="center">
-<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" 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="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>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b>
+<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
 </p>
 
 
 ## Intended Usage & Model Info
 
 `jina-embeddings-v2-base-en` is an English, monolingual **embedding model** supporting **8192 sequence length**.
-It is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
+It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
 The backbone `jina-bert-v2-base-en` is pretrained on the C4 dataset.
 The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives.
 These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
@@ -2634,17 +2634,11 @@ This makes our model useful for a range of use cases, especially when processing
 With a standard size of 137 million parameters, the model enables fast inference while delivering better performance than our small model. It is recommended to use a single GPU for inference.
 Additionally, we provide the following embedding models:
 
-**V1 (Based on T5, 512 Seq)**
-
-- [`jina-embeddings-v1-small-en`](https://huggingface.co/jinaai/jina-embedding-s-en-v1): 35 million parameters.
-- [`jina-embeddings-v1-base-en`](https://huggingface.co/jinaai/jina-embedding-b-en-v1): 110 million parameters.
-- [`jina-embeddings-v1-large-en`](https://huggingface.co/jinaai/jina-embedding-l-en-v1): 330 million parameters.
-
-**V2 (Based on JinaBert, 8k Seq)**
-
-- [`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 **(you are here)**.
-- [`jina-embeddings-v2-large-en`](): 435 million parameters (releasing soon).
+- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters **(you are here)**.
+- [`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 embedding model (releasing soon).
+- [`jina-embeddings-v2-base-de`](): German-English Bilingual embedding model (releasing soon).
+- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embedding model (releasing soon).
 
 ## Data & Parameters