readme: performance

README.md

@@ -120,7 +120,7 @@ library_name: transformers
 
 ## Quick Start
 
-The easiest way to starting using `jina-embeddings-v3` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
+The easiest way to start using `jina-embeddings-v3` is Jina AI's [Embedding API](https://jina.ai/embeddings/).
 
 
 ## Intended Usage & Model Info
@@ -201,7 +201,7 @@ embeddings = F.normalize(embeddings, p=2, dim=1)
 </p>
 </details>
 
-The easiest way to start using `jina-embeddings-v3` is Jina AI's [Embeddings API](https://jina.ai/embeddings/).
+The easiest way to start using `jina-embeddings-v3` is Jina AI's [Embedding API](https://jina.ai/embeddings/).
 
 Alternatively, you can use `jina-embeddings-v3` directly via Transformers package:
 ```python
@@ -254,17 +254,61 @@ The latest version (#todo: specify version) of SentenceTransformers also support
 from sentence_transformers import SentenceTransformer
 
 model = SentenceTransformer(
-    "jinaai/jina-embeddings-v3", trust_remote_code=True
+    "jinaai/jina-embeddings-v3",
+    prompts={
+        "retrieval.query": "Represent the query for retrieving evidence documents: ",
+        "retrieval.passage": "Represent the document for retrieval: ",
+    },
+    trust_remote_code=True
 )
 
-embeddings = model.encode(['How is the weather today?'], task_type='retrieval.query')
+embeddings = model.encode(['What is the weather like in Berlin today?'], task_type='retrieval.query')
 ```
 
 
 
 ## Performance
 
-TODO UPDATE THIS
+### English MTEB
+| Model                          | Average | Classification   | Clustering   | Pair Classification   | Reranking   | Retrieval   | STS  | Summarization   |
+|:------------------------------:|:-------:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
+| jina-embeddings-v2-en          | 58.12   | 68.82| 40.08| 84.44| 55.09| 45.64| 80.00| 30.56|
+| jina-embeddings-v3             | **65.60**   | **82.58**| 45.27| 84.01| 58.13| 53.87| **85.8** | 30.98|
+| text-embedding-3-large         | 62.03   | 75.45| 49.01| 84.22| 59.16| 55.44| 81.04| 29.92|
+| multilingual-e5-large-instruct | 64.41   | 77.56| 47.1 | 86.19| 58.58| 52.47| 84.78| 30.39|
+| Cohere-embed-multilingual-v3.0 | 60.08   | 64.01| 46.6 | 86.15| 57.86| 53.84| 83.15| 30.99|
+
+### Multilingual MTEB
+
+| Model                          | Average | Classification   | Clustering   | Pair Classification   | Reranking   | Retrieval   | STS  | Summarization   |
+|:------------------------------:|:-------:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
+| jina-embeddings-v2            | 60.54   | 65.69| 39.36| **82.95**| 66.57| 58.24| 66.6 | -    |
+| jina-embeddings-v3             | **64.44**   | **71.46**| 46.71| 76.91| 63.98| 57.98| **69.83**| -    |
+| multilingual-e5-large          | 59.58   | 65.22| 42.12| 76.95| 63.4 | 52.37| 64.65| -    |
+| multilingual-e5-large-instruct | 64.25   | 67.45| **52.12**| 77.79| **69.02**| **58.38**| 68.77| -    |
+
+
+### Long Context Tasks (LongEmbed)
+
+| Model                | Average | NarrativeQA | Needle | Passkey | QMSum | SummScreen | WikiQA |
+|:--------------------:|:-------:|:-----------:|:------:|:-------:|:-----:|:----------:|:------:|
+| jina-embeddings-v3*  | **70.39**   | 33.32       | **84.00**  | **100.00**  | **39.75** | 92.78      | 72.46  |
+| jina-embeddings-v2   | 58.12   | 37.89       | 54.25  | 50.25   | 38.87 | 93.48      | 73.99  |
+| text-embedding-3-large | 51.3  | 44.09       | 29.25  | 63.00   | 32.49 | 84.80      | 54.16  |
+| baai-bge-m3          | 56.56   | **45.76**       | 40.25  | 46.00   | 35.54 | **94.09**      | **77.73**  |
+
+**Notes:**
+- `*`: text-matching adapter
+
+
+#### Matryoshka Embeddings
+
+| Task        | 32   | 64   | 128  | 256  | 512  | 768  | 1024 |
+|:-------------:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
+| Retrieval     | 52.54| 58.54| 61.64| 62.72| 63.16| 63.30| 63.35|
+| STS           | 76.35| 77.03| 77.43| 77.56| 77.59| 77.59| 77.58|
+
+For a comprehensive evaluation and detailed metrics, please refer to the full paper available here (coming soon).
 
 ## Contact