chore: update readme

README.md

@@ -21684,53 +21684,6 @@ embeddings = model.encode(
 ```
 
 
-
-## Performance
-
-### English MTEB
-| Model                          |  Dimension  |  Average  | Classification   | Clustering   | Pair Classification   | Reranking   | Retrieval   | STS  | Summarization   |
-|:------------------------------:|:-----------:|:---------:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
-| jina-embeddings-v3             |    1024     | **65.60** | **82.58**| 45.27| 84.01| 58.13| 53.87| **85.8** | 30.98|
-| jina-embeddings-v2-en          |     768     |   58.12   |   68.82    | 40.08| 84.44| 55.09| 45.64| 80.00| 30.56|
-| text-embedding-3-large         |    3072     |   62.03   |   75.45    | 49.01| 84.22| 59.16| 55.44| 81.04| 29.92|
-| multilingual-e5-large-instruct |    1024     |   64.41   |   77.56   | 47.1 | 86.19| 58.58| 52.47| 84.78| 30.39|
-| Cohere-embed-multilingual-v3.0 | 1024 |   60.08   |   64.01   | 46.6 | 86.15| 57.86| 53.84| 83.15| 30.99|
-
-### Multilingual MTEB
-
-|             Model              | Dimension |  Average  | Classification | Clustering | Pair Classification | Reranking | Retrieval |    STS    | Summarization |
-|:------------------------------:|:---------:|:---------:|:--------------:|:----------:|:-------------------:|:---------:|:---------:|:---------:|:-------------:|
-|       jina-embeddings-v3       |   1024    | **64.44** |   **71.46**    |   46.71    |        76.91        |   63.98   |   57.98   | **69.83** |       -       |
-|     multilingual-e5-large      |   1024    |   59.58   |     65.22      |   42.12    |        76.95        |   63.4    |   52.37   |   64.65   |       -       |
-| multilingual-e5-large-instruct |   1024    |   64.25   |     67.45      | **52.12**  |        77.79        | **69.02** | **58.38** |   68.77   |       -       |
-
-
-### Long Context Tasks (LongEmbed)
-
-|         Model          | Dimension |  Average  | NarrativeQA |  Needle   |  Passkey   |   QMSum   | SummScreen |  WikiQA   |
-|:----------------------:|:---------:|:---------:|:-----------:|:---------:|:----------:|:---------:|:----------:|:---------:|
-|  jina-embeddings-v3*   |   1024    | **70.39** |    33.32    | **84.00** | **100.00** | **39.75** |   92.78    |   72.46   |
-|   jina-embeddings-v2   |    768    |   58.12   |    37.89    |   54.25   |   50.25    |   38.87   |   93.48    |   73.99   |
-| text-embedding-3-large |   3072    |   51.30   |    44.09    |   29.25   |   63.00    |   32.49   |   84.80    |   54.16   |
-|      baai-bge-m3       |   1024    |   56.56   |  **45.76**  |   40.25   |   46.00    |   35.54   | **94.09**  | **77.73** |
-
-Notes: `*`, use the text-matching adapter
-
-
-#### Matryoshka Embeddings
-
-|  Dimension  |  Retrieval  |   STS   |
-|:-----------:|:-----------:|:-------:|
-|     32      |    52.54    |  76.35  |
-|     64      |    58.54    |  77.03  |
-|     128     |    61.64    |  77.43  |
-|     256     |    62.72    |  77.56  |
-|     512     |    63.16    |  77.59  |
-|     768     |    63.3     |  77.59  |
-|    1024     |    63.35    |  77.58  |
-
-For a comprehensive evaluation and detailed metrics, please refer to the full paper available here (coming soon).
-
 ## Contact
 
 Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
@@ -21740,5 +21693,14 @@ Join our [Discord community](https://discord.jina.ai) and chat with other commun
 If you find `jina-embeddings-v3` useful in your research, please cite the following paper:
 
 ```bibtex
+@misc{sturua2024jinaembeddingsv3multilingualembeddingstask,
+      title={jina-embeddings-v3: Multilingual Embeddings With Task LoRA}, 
+      author={Saba Sturua and Isabelle Mohr and Mohammad Kalim Akram and Michael Günther and Bo Wang and Markus Krimmel and Feng Wang and Georgios Mastrapas and Andreas Koukounas and Andreas Koukounas and Nan Wang and Han Xiao},
+      year={2024},
+      eprint={2409.10173},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2409.10173}, 
+}
 
 ```